Online Domain Incremental Continual Learning (ODI-CL) refers to situations where the data distribution may change from one task to another. These changes can severely affect the learned model, focusing too much on previous data and failing to properly learn and represent new concepts. Conversely, ...
In this work, we propose the novel usage of Continual Learning (CL), in particular, using Domain-Incremental Learning (Domain-IL) settings, as a potent bias mitigation method to enhance the fairness of FER systems while guarding against biases arising from skewed data distributions. We compare ...
(1)灾难性遗忘 神经网络发生灾难性遗忘的本质是因为神经网络学习一个新任务时,需要更新网络中的参数,但是上一个任务提取出来的知识也是存储在这些参数上的。于是,神经网络在学习新任务时,旧任务的知识就会被覆盖。 (2) 多分布问题 数据之间存在分布差异,导致模型在训练数据上性能良好,但是在测试数据上性能下降。而且...
Continual / lifelong domain adaptation：与持续学习结合 Online / incremental domain adaptation：与在线和...
Mixture-of-Variational-Experts for Continual Learning hhihn/HVCL • • 25 Oct 2021 One weakness of machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge. 1 Paper Code Content...
domain expansion problem是continual learning problem的一部分。continual learning通常考虑multiple task learning或者sequence learning (more than two domains), 但是domain expansion problem只考虑两个domains,old 和 new domain。 Related work dropout method + maxout activation function 能够帮助减少遗忘学习到的信息...
Adaptive Online Domain Incremental Continual Learning Continual Learning (CL) problems pose significant challenges for Neural Network (NN)s. Online Domain Incremental Continual Learning (ODI-CL) refers to situations where the data distribution may change from one task to another. These chan... N Guna...
ICARL算法【 icarl: Incremental classifier and represen tation learning】,该算法使用教师网络和学生网络,以少量训练样本快速收敛所有已学习的任务。这种方法在学习新任务时只需要存储前一任务的少量样本,从而减少了存储开销。 GEM【 Gradient episodic memory for continual learning.】存储先前任务的梯度,确保新任务的梯...
ICARL算法【 icarl: Incremental classifier and represen tation learning】,该算法使用教师网络和学生网络,以少量训练样本快速收敛所有已学习的任务。这种方法在学习新任务时只需要存储前一任务的少量样本,从而减少了存储开销。 GEM【 Gradient episodic memory for continual learning.】存储先前任务的梯度,确保新任务的梯...
Continual learning Domain adaptation Lifelong learning Incremental learning Cross domain adaptation Unsupervised domain adaptation 1. Introduction The underlying goal of continual learning (CL) is to design a learning algorithm handing never-ending environments efficiently. Unlike conventional learning algorithms ...