The Latest Papers about AI
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PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
2021-11-24 18:59:58Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai YuAbstract
This paper explores a better codebook for BERT pre-training of vision transformers. The recent work BEiT successfully transfers BERT pre-training from NLP to the vision field. It directly adopts one simple discrete VAE as the visual tokenizer, but has not considered the semantic level of the resulting visual tokens. By contrast, the discrete tokens in NLP field are naturally highly semantic. This difference motivates us to learn a perceptual codebook. And we surprisingly find one simple yet effective idea: enforcing perceptual similarity during the dVAE training. We demonstrate that the visual tokens generated by the proposed perceptual codebook do exhibit better semantic meanings, and subsequently help pre-training achieve superior transfer performance in various downstream tasks. For example, we achieve 84.5 Top-1 accuracy on ImageNet-1K with ViT-B backbone, outperforming the competitive method BEiT by +1.3 with the same pre-training epochs. It can also improve the performance of object detection and segmentation tasks on COCO val by +1.3 box AP and +1.0 mask AP, semantic segmentation on ADE20k by +1.0 mIoU, The code and models will be available at \url{this https URL}.
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https://arxiv.org/abs/2111.12710
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MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation
2021-11-24 18:59:02Wenhao Li, Hong Liu, Hao Tang, Pichao Wang, Luc Van GoolAbstract
Estimating 3D human poses from monocular videos is a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spatial and temporal relationships. However, those works ignore the fact that it is an inverse problem where multiple feasible solutions (i.e., hypotheses) exist. To relieve this limitation, we propose a Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal representations of multiple plausible pose hypotheses. In order to effectively model multi-hypothesis dependencies and build strong relationships across hypothesis features, the task is decomposed into three stages: (i) Generate multiple initial hypothesis representations; (ii) Model self-hypothesis communication, merge multiple hypotheses into a single converged representation and then partition it into several diverged hypotheses; (iii) Learn cross-hypothesis communication and aggregate the multi-hypothesis features to synthesize the final 3D pose. Through the above processes, the final representation is enhanced and the synthesized pose is much more accurate. Extensive experiments show that MHFormer achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP. Without bells and whistles, its performance surpasses the previous best result by a large margin of 3% on Human3.6M. Code and models are available at this https URL.
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https://arxiv.org/abs/2111.12707
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MixSyn: Learning Composition and Style for Multi-Source Image Synthesis
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Synthetic images created by generative models increase in quality and expressiveness as newer models utilize larger datasets and novel architectures. Although this photorealism is a positive side-effect from a creative standpoint, it becomes problematic when such generative models are used for impersonation without consent. Most of these approaches are built on the partial transfer between source and target pairs, or they generate completely new samples based on an ideal distribution, still resembling the closest real sample in the dataset. We propose MixSyn (read as " mixin' ") for learning novel fuzzy compositions from multiple sources and creating novel images as a mix of image regions corresponding to the compositions. MixSyn not only combines uncorrelated regions from multiple source masks into a coherent semantic composition, but also generates mask-aware high quality reconstructions of non-existing images. We compare MixSyn to state-of-the-art single-source sequential generation and collage generation approaches in terms of quality, diversity, realism, and expressive power; while also showcasing interactive synthesis, mix & match, and edit propagation tasks, with no mask dependency.
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https://arxiv.org/abs/2111.12705
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Investigating Tradeoffs in Real-World Video Super-Resolution
2021-11-24 18:58:21Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change LoyAbstract
The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairing output quality. To balance the tradeoff between detail synthesis and artifact suppression, we found an image pre-cleaning stage indispensable to reduce noises and artifacts prior to propagation. Equipped with a carefully designed cleaning module, our RealBasicVSR outperforms existing methods in both quality and efficiency. Second, real-world VSR models are often trained with diverse degradations to improve generalizability, requiring increased batch size to produce a stable gradient. Inevitably, the increased computational burden results in various problems, including 1) speed-performance tradeoff and 2) batch-length tradeoff. To alleviate the first tradeoff, we propose a stochastic degradation scheme that reduces up to 40\% of training time without sacrificing performance. We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns. Our dataset can serve as a common ground for benchmarking. Code, models, and the dataset will be made publicly available.
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https://arxiv.org/abs/2111.12704
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Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
2021-11-24 18:56:27Tong Wu, Liang Pan, Junzhe Zhang, Tai Wang, Ziwei Liu, Dahua LinAbstract
Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mismatched local density, and EMD is usually dominated by global distribution while overlooks the fidelity of detailed structures. Besides, their unbounded value range induces a heavy influence from the outliers. These defects prevent them from providing a consistent evaluation. To tackle these problems, we propose a new similarity measure named Density-aware Chamfer Distance (DCD). It is derived from CD and benefits from several desirable properties: 1) it can detect disparity of density distributions and is thus a more intensive measure of similarity compared to CD; 2) it is stricter with detailed structures and significantly more computationally efficient than EMD; 3) the bounded value range encourages a more stable and reasonable evaluation over the whole test set. We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other. We can also use DCD as the training loss, which outperforms the same model trained with CD loss on all three metrics. In addition, we propose a novel point discriminator module that estimates the priority for another guided down-sampling step, and it achieves noticeable improvements under DCD together with competitive results for both CD and EMD. We hope our work could pave the way for a more comprehensive and practical point cloud similarity evaluation. Our code will be available at: this https URL .
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https://arxiv.org/abs/2111.12702
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Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
2021-11-24 18:55:14Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, Chris G. WillcocksAbstract
Whilst diffusion probabilistic models can generate high quality image content, key limitations remain in terms of both generating high-resolution imagery and their associated high computational requirements. Recent Vector-Quantized image models have overcome this limitation of image resolution but are prohibitively slow and unidirectional as they generate tokens via element-wise autoregressive sampling from the prior. By contrast, in this paper we propose a novel discrete diffusion probabilistic model prior which enables parallel prediction of Vector-Quantized tokens by using an unconstrained Transformer architecture as the backbone. During training, tokens are randomly masked in an order-agnostic manner and the Transformer learns to predict the original tokens. This parallelism of Vector-Quantized token prediction in turn facilitates unconditional generation of globally consistent high-resolution and diverse imagery at a fraction of the computational expense. In this manner, we can generate image resolutions exceeding that of the original training set samples whilst additionally provisioning per-image likelihood estimates (in a departure from generative adversarial approaches). Our approach achieves state-of-the-art results in terms of Density (LSUN Bedroom: 1.51; LSUN Churches: 1.12; FFHQ: 1.20) and Coverage (LSUN Bedroom: 0.83; LSUN Churches: 0.73; FFHQ: 0.80), and performs competitively on FID (LSUN Bedroom: 3.64; LSUN Churches: 4.07; FFHQ: 6.11) whilst offering advantages in terms of both computation and reduced training set requirements.
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https://arxiv.org/abs/2111.12701
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Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
2021-11-24 18:50:47Dat Huynh, Jason Kuen, Zhe Lin, Jiuxiang Gu, Ehsan ElhamifarAbstract
Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. It is an important step toward reducing laborious human supervision. Most existing works first pretrain a model on captioned images covering many novel classes and then finetune it on limited base classes with mask annotations. However, the high-level textual information learned from caption pretraining alone cannot effectively encode the details required for pixel-wise segmentation. To address this, we propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images. Thus, our framework is capable of labeling novel classes in captions via their word semantics to self-train a student model. To account for noises in pseudo masks, we design a robust student model that selectively distills mask knowledge by estimating the mask noise levels, hence mitigating the adverse impact of noisy pseudo masks. By extensive experiments, we show the effectiveness of our framework, where we significantly improve mAP score by 4.5% on MS-COCO and 5.1% on the large-scale Open Images & Conceptual Captions datasets compared to the state-of-the-art.
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https://arxiv.org/abs/2111.12698
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A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
2021-11-24 18:48:03Ce Zheng, Matias Mendieta, Pu Wang, Aidong Lu, Chen ChenAbstract
Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model size are often neglected, despite being key characteristics for practical use of human mesh reconstruction models (e.g. virtual try-on systems). In this paper, we present GTRS, a lightweight pose-based method that can reconstruct human mesh from 2D human pose. We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh. We demonstrate the efficiency and generalization of GTRS by extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS achieves better accuracy than the SOTA pose-based method Pose2Mesh while only using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging in-the-wild 3DPW dataset. Code will be publicly available.
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https://arxiv.org/abs/2111.12696
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Automatic Mapping with Obstacle Identification for Indoor Human Mobility Assessment
2021-11-24 18:36:33V. Ayala-Alfaro, J. A. Vilchis-Mar, F. E. Correa-Tome, J. P. Ramirez-ParedesAbstract
We propose a framework that allows a mobile robot to build a map of an indoor scenario, identifying and highlighting objects that may be considered a hindrance to people with limited mobility. The map is built by combining recent developments in monocular SLAM with information from inertial sensors of the robot platform, resulting in a metric point cloud that can be further processed to obtain a mesh. The images from the monocular camera are simultaneously analyzed with an object recognition neural network, tuned to detect a particular class of targets. This information is then processed and incorporated on the metric map, resulting in a detailed survey of the locations and bounding volumes of the objects of interest. The result can be used to inform policy makers and users with limited mobility of the hazards present in a particular indoor location. Our initial tests were performed using a micro-UAV and will be extended to other robotic platforms.
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https://arxiv.org/abs/2111.12690
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A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine
2021-11-24 18:36:28David Solis-Martin, Juan Galan-Paez, Joaquin Borrego-DiazAbstract
This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to extract a low-dimensional feature vector using the normalized raw data as input. The second DCNN ingests a list of vectors taken from the former DCNN and estimates the RUL. Model selection was carried out by means of Bayesian optimization using a repeated random subsampling validation approach. The proposed methodology was ranked in the third place of the 2021 PHM Conference Data Challenge.
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https://arxiv.org/abs/2111.12689
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EgoRenderer: Rendering Human Avatars from Egocentric Camera Images
2021-11-24 18:33:02Tao Hu, Kripasindhu Sarkar, Lingjie Liu, Matthias Zwicker, Christian TheobaltAbstract
We present EgoRenderer, a system for rendering full-body neural avatars of a person captured by a wearable, egocentric fisheye camera that is mounted on a cap or a VR headset. Our system renders photorealistic novel views of the actor and her motion from arbitrary virtual camera locations. Rendering full-body avatars from such egocentric images come with unique challenges due to the top-down view and large distortions. We tackle these challenges by decomposing the rendering process into several steps, including texture synthesis, pose construction, and neural image translation. For texture synthesis, we propose Ego-DPNet, a neural network that infers dense correspondences between the input fisheye images and an underlying parametric body model, and to extract textures from egocentric inputs. In addition, to encode dynamic appearances, our approach also learns an implicit texture stack that captures detailed appearance variation across poses and viewpoints. For correct pose generation, we first estimate body pose from the egocentric view using a parametric model. We then synthesize an external free-viewpoint pose image by projecting the parametric model to the user-specified target viewpoint. We next combine the target pose image and the textures into a combined feature image, which is transformed into the output color image using a neural image translation network. Experimental evaluations show that EgoRenderer is capable of generating realistic free-viewpoint avatars of a person wearing an egocentric camera. Comparisons to several baselines demonstrate the advantages of our approach.
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https://arxiv.org/abs/2111.12685
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VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
2021-11-24 18:31:20Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, Zicheng LiuAbstract
A great challenge in video-language (VidL) modeling lies in the disconnection between fixed video representations extracted from image/video understanding models and downstream VidL data. Recent studies try to mitigate this disconnection via end-to-end training. To make it computationally feasible, prior works tend to "imagify" video inputs, i.e., a handful of sparsely sampled frames are fed into a 2D CNN, followed by a simple mean-pooling or concatenation to obtain the overall video representations. Although achieving promising results, such simple approaches may lose temporal information that is essential for performing downstream VidL tasks. In this work, we present VIOLET, a fully end-to-end VIdeO-LanguagE Transformer, which adopts a video transformer to explicitly model the temporal dynamics of video inputs. Further, unlike previous studies that found pre-training tasks on video inputs (e.g., masked frame modeling) not very effective, we design a new pre-training task, Masked Visual-token Modeling (MVM), for better video modeling. Specifically, the original video frame patches are "tokenized" into discrete visual tokens, and the goal is to recover the original visual tokens based on the masked patches. Comprehensive analysis demonstrates the effectiveness of both explicit temporal modeling via video transformer and MVM. As a result, VIOLET achieves new state-of-the-art performance on 5 video question answering tasks and 4 text-to-video retrieval tasks.
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https://arxiv.org/abs/2111.12681
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An XGBoost-Based Forecasting Framework for Product Cannibalization
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Two major challenges in demand forecasting are product cannibalization and long term forecasting. Product cannibalization is a phenomenon in which high demand of some products leads to reduction in sales of other products. Long term forecasting involves forecasting the sales over longer time frame that is critical for strategic business purposes. Also, conventional methods, for instance, recurrent neural networks may be ineffective where train data size is small as in the case in this study. This work presents XGBoost-based three-stage framework that addresses product cannibalization and associated long term error propagation problems. The performance of the proposed three-stage XGBoost-based framework is compared to and is found superior than that of regular XGBoost algorithm.
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https://arxiv.org/abs/2111.12680
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Reinforcement Learning for General LTL Objectives Is Intractable
2021-11-24 18:26:13Cambridge Yang, Michael Littman, Michael CarbinAbstract
In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objectives. Despite these advancements, there are fundamental limitations to how well this problem can be solved that previous studies have alluded to but, to our knowledge, have not examined in depth. In this paper, we address theoretically the hardness of learning with general LTL objectives. We formalize the problem under the probably approximately correct learning in Markov decision processes (PAC-MDP) framework, a standard framework for measuring sample complexity in reinforcement learning. In this formalization, we prove that the optimal policy for any LTL formula is PAC-MDP-learnable only if the formula is in the most limited class in the LTL hierarchy, consisting of only finite-horizon-decidable properties. Practically, our result implies that it is impossible for a reinforcement-learning algorithm to obtain a PAC-MDP guarantee on the performance of its learned policy after finitely many interactions with an unconstrained environment for non-finite-horizon-decidable LTL objectives.
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https://arxiv.org/abs/2111.12679
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Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
2021-11-24 18:07:33Nicolai Dorka, Joschka Boedecker, Wolfram BurgardAbstract
Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on temporal difference learning typically are prone to an over- or underestimation bias building up over time. In this paper, we propose a general method called Adaptively Calibrated Critics (ACC) that uses the most recent high variance but unbiased on-policy rollouts to alleviate the bias of the low variance temporal difference targets. We apply ACC to Truncated Quantile Critics, which is an algorithm for continuous control that allows regulation of the bias with a hyperparameter tuned per environment. The resulting algorithm adaptively adjusts the parameter during training rendering hyperparameter search unnecessary and sets a new state of the art on the OpenAI gym continuous control benchmark among all algorithms that do not tune hyperparameters for each environment. Additionally, we demonstrate that ACC is quite general by further applying it to TD3 and showing an improved performance also in this setting.
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https://arxiv.org/abs/2111.12673
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MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning
2021-11-24 17:51:29Siladittya Manna, Saumik Bhattacharya, Umapada PalAbstract
Self-supervised contrastive learning is one of the domains which has progressed rapidly over the last few years. Most of the state-of-the-art self-supervised algorithms use a large number of negative samples, momentum updates, specific architectural modifications, or extensive training to learn good representations. Such arrangements make the overall training process complex and challenging to realize analytically. In this paper, we propose a mutual information optimization based loss function for contrastive learning where we model contrastive learning into a binary classification problem to predict if a pair is positive or not. This formulation not only helps us to track the problem mathematically but also helps us to outperform existing algorithms. Unlike the existing methods that only maximize the mutual information in a positive pair, the proposed loss function optimizes the mutual information in both positive and negative pairs. We also present a mathematical expression for the parameter gradients flowing into the projector and the displacement of the feature vectors in the feature space. This helps us to get a mathematical insight into the working principle of contrastive learning. An additive $L_2$ regularizer is also used to prevent diverging of the feature vectors and to improve performance. The proposed method outperforms the state-of-the-art algorithms on benchmark datasets like STL-10, CIFAR-10, CIFAR-100. After only 250 epochs of pre-training, the proposed model achieves the best accuracy of 85.44\%, 60.75\%, 56.81\% on CIFAR-10, STL-10, CIFAR-100 datasets, respectively.
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https://arxiv.org/abs/2111.12664
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Analysing Statistical methods for Automatic Detection of Image Forgery
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Image manipulation and forgery detection have been a topic of research for more than a decade now. New-age tools and large-scale social platforms have given space for manipulated media to thrive. These media can be potentially dangerous and thus innumerable methods have been designed and tested to prove their robustness in detecting forgery. However, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only with particular datasets. In this work, we analyze the issue of out-of-distribution generalisability of the current state-of-the-art image forgery detection techniques through several experiments. Our study focuses on models that utilise handcrafted features for image forgery detection. We show that the developed methods fail to perform well on cross-dataset evaluations and in-the-wild manipulated media. As a consequence, a question is raised about the current evaluation and overestimated performance of the systems under consideration. Note: This work was done during a summer research internship at ITMR Lab, IIIT-Allahabad under the supervision of Prof. Anupam Agarwal.
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https://arxiv.org/abs/2111.12661
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SM3D: Simultaneous Monocular Mapping and 3D Detection
2021-11-24 17:23:37Runfa Li, Truong NguyenAbstract
Mapping and 3D detection are two major issues in vision-based robotics, and self-driving. While previous works only focus on each task separately, we present an innovative and efficient multi-task deep learning framework (SM3D) for Simultaneous Mapping and 3D Detection by bridging the gap with robust depth estimation and "Pseudo-LiDAR" point cloud for the first time. The Mapping module takes consecutive monocular frames to generate depth and pose estimation. In 3D Detection module, the depth estimation is projected into 3D space to generate "Pseudo-LiDAR" point cloud, where LiDAR-based 3D detector can be leveraged on point cloud for vehicular 3D detection and localization. By end-to-end training of both modules, the proposed mapping and 3D detection method outperforms the state-of-the-art baseline by 10.0% and 13.2% in accuracy, respectively. While achieving better accuracy, our monocular multi-task SM3D is more than 2 times faster than pure stereo 3D detector, and 18.3% faster than using two modules separately.
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https://arxiv.org/abs/2111.12643
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EAD: an ensemble approach to detect adversarial examples from the hidden features of deep neural networks
2021-11-24 17:05:26Francesco Craighero, Fabrizio Angaroni, Fabio Stella, Chiara Damiani, Marco Antoniotti, Alex GraudenziAbstract
One of the key challenges in Deep Learning is the definition of effective strategies for the detection of adversarial examples. To this end, we propose a novel approach named Ensemble Adversarial Detector (EAD) for the identification of adversarial examples, in a standard multiclass classification scenario. EAD combines multiple detectors that exploit distinct properties of the input instances in the internal representation of a pre-trained Deep Neural Network (DNN). Specifically, EAD integrates the state-of-the-art detectors based on Mahalanobis distance and on Local Intrinsic Dimensionality (LID) with a newly introduced method based on One-class Support Vector Machines (OSVMs). Although all constituting methods assume that the greater the distance of a test instance from the set of correctly classified training instances, the higher its probability to be an adversarial example, they differ in the way such distance is computed. In order to exploit the effectiveness of the different methods in capturing distinct properties of data distributions and, accordingly, efficiently tackle the trade-off between generalization and overfitting, EAD employs detector-specific distance scores as features of a logistic regression classifier, after independent hyperparameters optimization. We evaluated the EAD approach on distinct datasets (CIFAR-10, CIFAR-100 and SVHN) and models (ResNet and DenseNet) and with regard to four adversarial attacks (FGSM, BIM, DeepFool and CW), also by comparing with competing approaches. Overall, we show that EAD achieves the best AUROC and AUPR in the large majority of the settings and comparable performance in the others. The improvement over the state-of-the-art, and the possibility to easily extend EAD to include any arbitrary set of detectors, pave the way to a widespread adoption of ensemble approaches in the broad field of adversarial example detection.
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https://arxiv.org/abs/2111.12631
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Efficient Decompositional Rule Extraction for Deep Neural Networks
2021-11-24 16:54:10Mateo Espinosa Zarlenga, Zohreh Shams, Mateja JamnikAbstract
In recent years, there has been significant work on increasing both interpretability and debuggability of a Deep Neural Network (DNN) by extracting a rule-based model that approximates its decision boundary. Nevertheless, current DNN rule extraction methods that consider a DNN's latent space when extracting rules, known as decompositional algorithms, are either restricted to single-layer DNNs or intractable as the size of the DNN or data grows. In this paper, we address these limitations by introducing ECLAIRE, a novel polynomial-time rule extraction algorithm capable of scaling to both large DNN architectures and large training datasets. We evaluate ECLAIRE on a wide variety of tasks, ranging from breast cancer prognosis to particle detection, and show that it consistently extracts more accurate and comprehensible rule sets than the current state-of-the-art methods while using orders of magnitude less computational resources. We make all of our methods available, including a rule set visualisation interface, through the open-source REMIX library (this https URL).
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https://arxiv.org/abs/2111.12628
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