Abstract
Online task-free continual learning (OTFCL) is a more challenging variant of continual learning which emphasizes the gradual shift of task boundaries and learns in an online mode. Existing methods rely on a memory buffer composed of old samples to prevent forgetting. However,the use of memory buffers not only raises privacy concerns but also hinders the efficient learning of new samples. To address this problem, we propose a novel framework called I2CANSAY that gets rid of the dependence on memory buffers and efficiently learns the knowledge of new data from one-shot samples. Concretely, our framework comprises two main modules. Firstly, the Inter-Class Analogical Augmentation (ICAN) module generates diverse pseudo-features for old classes based on the inter-class analogy of feature distributions for different new classes, serving as a substitute for the memory buffer. Secondly, the Intra-Class Significance Analysis (ISAY) module analyzes the significance of attributes for each class via its distribution standard deviation, and generates the importance vector as a correction bias for the linear classifier, thereby enhancing the capability of learning from new samples. We run our experiments on four popular image classification datasets: CoRe50, CIFAR-10, CIFAR-100, and CUB-200, our approach outperforms the prior state-of-the-art by a large margin.
Abstract (translated)
在线无任务持续学习（OTFCL）是一种更具挑战性的连续学习变体，它强调了任务边界的逐步转变和以在线方式学习。现有的方法依赖于由旧样本组成的记忆缓冲区来防止遗忘。然而，使用记忆缓冲区不仅引发了隐私问题，而且还会阻碍对新样本的有效学习。为解决这个问题，我们提出了一个名为I2CANSAY的新框架，它消除了对记忆缓冲区的依赖，并有效地从一挥发性样本中学习新数据的知識。具体来说，我们的框架由两个主要模块组成。首先，跨类别相似性增强（ICAN）模块根据不同新类之间的类內聚类相似性生成多样伪特征，作为记忆缓冲区的补充。其次，内类重要性分析（ISAY）模块通过分布标准差分析每个类的属性，并生成修正偏差向量作为线性分类器的增强剂，从而提高从新样本中学习的可能性。我们对四个流行的图像分类数据集：CoRe50，CIFAR-10，CIFAR-100和CUB-200）进行实验，我们的方法在很大程度上超过了先前的 state-of-the-art 水平。
URL
https://arxiv.org/abs/2404.13576
