Paper Reading AI Learner

Continual Learning From Unlabeled Data Via Deep Clustering

2021-04-14 23:46:17
Jiangpeng He, Fengqing Zhu


tract: Continual learning, a promising future learning strategy, aims to learn new tasks incrementally using less computation and memory resources instead of retraining the model from scratch whenever new task arrives. However, existing approaches are designed in supervised fashion assuming all data from new tasks have been annotated, which are not practical for many real-life applications. In this work, we introduce a new framework to make continual learning feasible in unsupervised mode by using pseudo label obtained from cluster assignments to update model. We focus on image classification task under class-incremental setting and assume no class label is provided for training in each incremental learning step. For illustration purpose, we apply k-means clustering, knowledge distillation loss and exemplar set as our baseline solution, which achieves competitive results even compared with supervised approaches on both challenging CIFAR-100 and ImageNet (ILSVRC) datasets. We also demonstrate that the performance of our baseline solution can be further improved by incorporating recently developed supervised continual learning techniques, showing great potential for our framework to minimize the gap between supervised and unsupervised continual learning.

Abstract (translated)



3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot