Webrehearsal-based methods MIR, ASER, and SCR. We also demonstrate that RAR successfully achieves an accurate approximation of the loss landscape of past data and high-loss ridge aversion in its learning trajectory. Extensive ablation studies are conducted to study the interplay between repeated and augmented rehearsal, and Web13 mrt. 2024 · 这个领域的改进主要集中在：storing diverse examples（存储不同的例子），如Gradient-based Sample Selection (GSS) ，replaying examples with larger estimated “interference”（用较大的估计“干扰”重播例子），如 Maximally Interfered Retrieval (MIR)，相比之下，GMED与基于内存的方法一起使用，并显式地搜索一个编辑过的例 …
Diverse Memory for Experience Replay in Continual Learning
Web8 mrt. 2024 · # Methodology : Maximally Interfered Retrieval 크게 (1) Replay Memory 에서의 방법과 (2) Generative Model 에서의 방법으로 나뉜다. Main idea는 과거의 샘플에서 랜덤하게 뽑지 말고, new incoming sample들에게 maximally interfered 되는 샘플을 Memory buffer M M 에서 뽑자는 것 이다. CIL 상황을 가정할 때, 기존의 방법 (랜덤추출, 좌)과 MIR … WebMaximally Interfered Retrieval (MIR, NeurIPS 2024) Non-iid (Non independent identically distribution) 란 각 데이터가 독립 + 동일한 확률분포를 가지고 있지 않다는 의미를 지니고 있다. MIR 업데이트는 random replay와 같지만 data 회수는 현재 새로운 데이터와 가장 간섭 (interference)이 큰 date를 sampling하여 사용한다. mariners clinic
online-continual-learning-with-maximal-interfered-retrieval
Webexamples with larger estimated “interference” as in Maximally Interfered Retrieval (MIR) with experience replay [2]. In contrast , GMED is used in conjunction with memory-based approaches and explicitly searches for an edited example which is optimized to be more “effective” for replay. Replay Example Construction. Web7 sep. 2024 · GD umb , Experience Replay (ER) , and Maximally Interfered Retrieval … WebMaximally_Interfered_Retrieval/gen_main.py at master · … dalton neil pa mn