I2CANSAY：面向无样本在线无任务持续学习的类间类比增强与类内显著性分析
I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
AI总结：
针对在线无任务持续学习中记忆缓冲区带来的隐私与效率问题，提出 I2CANSAY，通过类间伪特征增强和类内显著性校正实现无缓冲区单次学习，并在四个图像数据集上显著超越现有方法。
摘要：
在线无任务持续学习（OTFCL）是持续学习中一种更具挑战性的变体，它强调任务边界的逐渐转移，并以在线模式进行学习。现有方法依赖由旧样本组成的记忆缓冲区来防止遗忘。然而，记忆缓冲区的使用不仅引发隐私担忧，还会阻碍对新样本的高效学习。为解决这一问题，我们提出了一个名为 I2CANSAY 的新颖框架，它摆脱了对记忆缓冲区的依赖，并能从单次样本中高效学习新数据的知识。具体而言，该框架包含两个主要模块。首先，类间类比增强（ICAN）模块基于不同新类特征分布之间的类间类比，为旧类生成多样化的伪特征，以此作为记忆缓冲区的替代。其次，类内显著性分析（ISAY）模块通过各类属性的分布标准差分析其显著性，并生成重要性向量作为线性分类器的校正偏置，从而增强从新样本中学习的能力。我们在四个常用图像分类数据集上进行实验：CoRe50、CIFAR-10、CIFAR-100 和 CUB-200，所提方法以较大优势超越此前的最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
