Online class incremental learning? 2024 Elsevier LtdAiming at the realization of learning continually from an online data stream, replay-based methods have shown superior potential. The main challenge of replay-based methods is the selection of representative samples which are stored in the buffer and...
为了解决这些挑战,论文提出了一种新的ER方法,即Adversarial Shapley Value Experience Replay (ASER),它使用Shapley值来评估记忆样本的贡献,并在记忆检索和更新中采用对抗性策略。 3.Efficient Computation of Shapley Value via KNN Classifier 此章节主要介绍了如何高效地计算K最近邻分类器中每个数据点的Shapley值。 KNN...
S-LORA: SCALABLE LOW-RANK ADAPTATION FOR CLASS INCREMENTAL LEARNING 链接:arxiv.org/pdf/2501.1319 发表:ICLR 2025 研究问题:连续学习(CL, Continual Learning)是在sequential tasks,具有non-stationary data distribution上进行模型的训练。常见的问题可能有catastrophic forgetting,就是在训练新任务的时候,在之前任务上...
ICICLE: Interpretable Class Incremental Continual Learning Dawid Rymarczyk1,2,3,∗ Joost van de Weijer4,5 Bartosz Zielin´ski1,3,6 Bartłomiej Twardowski4,5,6 1 Faculty of Mathematics and Computer Science, Jagiellonian University 2 Doctoral School of Exact and Life Sci...
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning Huiwei Lin, Baoquan Zhang, Shanshan Feng*, Xutao Li, Yunming Ye Harbin Institute of Technology, Shenzhen {linhuiwei, zhangbaoquan}@stu.hit.edu.cn, {victor fengss, lixutao, y...
近年来，深度学习领域对终身学习（LLL）的关注激增，通常称为持续学习（Continual Learning）。尽管深度神经网络（DNNs）在许多任务中表现出色，但基于联结主义的深度学习算法面临灾难性遗忘问题，导致实现持续学习困难。在序列学习中，模型在学习新任务后，旧任务性能显著下降。人脑却能学习大量不同任务而不受...
2. Two problems with the current approach to class-incremental continual learning 3. Methods and 3.1. Infinite dSprites 3.2. Disentangled learning 4. Related work 4.1. Continual learning and 4.2. Benchmarking continual learning 5. Experiments ...
Class-Incremental Learning refers to the process of learning in real-world scenarios where data is limited and new learning data is continuously presented. It involves categorizing approaches into different families to balance performance, scalability, efficiency, and complexity in the field of deep lear...
there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption. While memory replay techniques have shown exceptional promise for this task of continual learning, the best method for selecting which buffered images to replay is still...
Continual Learning with foundation models has recently emerged as a promising approach to harnessing the power of pre-trained models for sequential tasks. Existing prompt-based methods generally use a gating mechanism to select relevant prompts aligned with the test query for further processing. However...