  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.
