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...
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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...
ONLINE CLASS-INCREMENTAL CONTINUAL LEARNING WITH ADVERSARIAL SHAPLEY VALUEA method for scoring training data samples according to an ability to preserve latent decision boundaries for previously observed classes while promoting learning from an input batch of new images from an online data stream, ...
MORE (A Multi-head Model for cOntinual REplay)和ROW (Replay with Out-of-distribution Weights):特别关注于通过重放机制和OOD(Out-Of-Distribution)检测,来改善类别间的判别和任务ID预测。 这些对比方法覆盖了持续学习领域内多种主要的策略,包括重放机制、知识蒸馏、参数隔离和注意力机制等。通过与这些方法的对比...
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 ...
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, ...
1. Federated Continual Learning with Weighted Inter-client Transfer (ICML 2021)该工作提出了一种联邦...
技术标签:机器学习continual learning论文解析 乔治亚州研究所提出的,发表在ICLR2021上。针对无监督的类别增量提出了本文的方法。本文基于无监督,增量数据没有标签,因此范畴就是选用了增量学习之中最宽松的限制,即基于样本回放的增量学习方法,旧样本会被存储在模型Memory之中。 论文地址:[2104.04450] Unsupervised Class-I...
Continual learning addresses this challenge as it aims to build models that can integrate new knowledge over time while preserving previously acquired knowledge. In the context of class- incremental learning (CIL), training a classification model is a sequential process where each step consists in...