2)Maximally-interfered Retrieval (MIR) MIR 是一种更高级的ER方法,它选择那些在当前任务更新后损失增加最多的样本进行回放,以最大化对新任务的干扰。 3)Gradient-based Sample Selection (GSS) GSS 关注于如何更新记忆缓冲区中的样本,以确保样本的梯度多样性,从而提高学习效率。 尽管ER方法在持续学习中很有前景,...
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...
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...
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 an open question. In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new...
Online Class-Incremental Continual Learning with Adversarial Shapley Value (AAAI 2021) Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning (CVPR2021 Workshop) Online Continual Learning in Image Classification: An Empirical Survey (Neurocomp...
Constructing Enhanced Mutual Information for Online Class-Incremental Learning Online Class-Incremental continual Learning (OCIL) addresses the challenge of continuously learning from a single-channel data stream, adapting to new task... H Zhang,F Lyu,S Fan,... 被引量: 0发表: 2024年 Mitigating ...
We then construct the exemplar set by using the first q samples in each class {E1(y), ...Eq(y)}, y ∈ [1, ..., n] where q is manually speci- fied. The exemplar set is commonly used to help retain the old classes' knowledge in incremental learning methods. Figure 3: Modified ...
We test on two kinds of online-continual auto-associative tasks: online class incremental (OCI) and online domain incremental (ODI). In both, data from each task is presented incrementally, one task at a time. In the OCI setting, each task consists of images from the same class. In the...
1.数据流:Online Continual Learning的训练数据以数据流的形式不断输入模型。数据流可以是实时产生的,也可以是历史数据的实时更新。 2.模型更新:在接收到新的数据后,模型会对其进行处理并更新参数。更新的目标是使模型在处理新数据时能够更好地拟合数据分布。 3.自适应学习:Online Continual Learning允许模型根据新数据...
Memory Aware Synapses（MAS）和Intelligent Synapses（IS）则分别通过参数重要性的不同度量来实现持续学习。MAS关注参数改变对模型输出的影响，而IS则考虑参数改变对损失函数的影响。这两种方法在计算参数重要性时采取不同的策略，以适应不同场景下的学习需求。持续学习技术的关键在于平衡学习新任务和保留旧...