PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017 - mm-ee/pytorch-deep-generative-replay
这也是使用了 Generative Replay 思路的地方。但作者发现这样仍然会出现问题:如下图中间的图所示,只进行如上操作效果并不好,不仅遗忘不彻底,还会把别的类带歪掉。于是作者想到了一个新的解决方案,去寻找一个新的分布来替代想要被遗忘的分布。下图中的最右侧就是使用了 Uniform Distribution 来替代原有的 0 的...
Generative Replay (GR):通过生成模型生成数据样本来模拟“记住”的数据,这些样本在训练过程中被用来帮助模型维持对遗忘以外概念的生成能力。 3. 定义优化目标 目标函数构建:构建一个目标函数,该函数旨在最小化要遗忘的概念数据的条件似然,同时最大化其他数据的条件似然。这是通过一个结合了EWC正则化项和GR数据生成项...
Deep generative modelsAutomatic speaker verification (ASV) systems are highly vulnerable to presentation attacks, also called spoofing attacks. Replay is among the simplest attacks to mount - yet difficult to detect reliably. The generalization failure of spoofing countermeasures (CMs) has driven the ...
Deep Generative Replay (DGR):使用一个深度生成模型(如生成对抗网络)来生成旧任务的数据,并与当前任务的真实数据一起训练。 Replay through Feedback (RfF):通过反馈连接使用单个模型进行生成式重放,该模型同时处理分类和数据生成。 Incremental Learning using Conditional Adversarial Networks (ILCAN):使用条件对抗网络...
Kim. Continual learning with deep generative replay. In NeurIPS, 2017. [60] K. Shmelkov, C. Schmid, and K. Alahari. How good is my GAN? In ECCV, 2018. [61] K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside con- volutional networks: Visualising image classification models ...
《Continual Learning with Deep Generative Replay》H Shin, J K Lee, J Kim, J Kim [MIT & SK T-Brain] (2017) http://t.cn/RS7y8FU
《Continual Learning with Deep Generative Replay》DGRarxiv.org/pdf/1705.0869 一个scholar 包括一个 generator 和一个 solver。针对一个新的任务,generator 学习重构累积样本输入空间;solver 学习输入输出间的映射。 这里重演的目标值是之前 solver 对过去样本的回复,即黄色部分。同时验证和训练损失值不同: conclusi...
generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two ...
continual semi-supervised learning, its components, anomaly detection extension, and the training protocols; the paper introduces a baseline model of a variational autoencoder (VAE) to work with semi-supervised data along with a continual learning method of deep generative replay with outlier rejection...