Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency
By Yanan Gu, Xu Yang, Kun-Juan Wei, Cheng Deng. 2022
106 citations recorded by the source corpus.
Online class-incremental continual learning aims to learn new classes continually from a never-ending and single-pass data stream, while not forgetting the learned knowledge of old classes. Existing replay-based methods have shown promising performance by storing a subset of old class data. Unfortunately, these methods only focus on selecting samples from the memory bank for replay and ignore the adequate exploration of semantic information in the single-pass data stream, leading to poor classification accuracy. In this paper, we propose a novel yet effective framework for online class-incremental continual learning, which considers not only the selection of stored samples, but also the full exploration of the data stream. Specifically, we propose a gradient-based sample selection strategy, which selects the stored samples whose gradients generated in the network are most interfered by the new incoming samples. We believe such samples are beneficial for updating the neural network based on back gradient propagation. More importantly, we seek to explore the semantic information between two different views of training images by maximizing their mutual information, which is conducive to the improvement of classification accuracy. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on a variety of benchmark datasets. Our code is available on https://github.com/YananGu/DVC.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning, Meta & Continual Learning
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Citations in the corpus
- A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, methodology): “Meanwhile, the memory buffer can be dynamically managed, such as by removing less important training samples (CLIB [224]), editing the old training samples (GMED [191]) or their distributions (DRO [443]) to be more likely forgotten, and retrieving the old training samples that are susceptible to int”
- Online Prototype Learning for Online Continual Learning (2023, methodology): “In addition, for DER++[4], SCR [44], and DVC [25], we follow their default settings and use their own extra data augmentations.”
- Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation (2024, methodology): “We use two commonly used metrics Average Accuracy ( ACC ) and Average Forgetting ( AF ) to evaluate their performance on benchmarks following [20, 21, 41, 51, 52]: where a i,j is the test accuracy of task i when the model has been trained on task from 1 to j , and T is the total number of tasks.”