Differences

This shows you the differences between two versions of the page.

--- deep_learning:incremental_learning [2021/08/01 17:45]
jordan [Key Challenge]
+++ deep_learning:incremental_learning [2023/03/08 16:04] (current)
xujianglong ↷ Page moved from 内部资料:deep_learning:incremental_learning to deep_learning:incremental_learning
@@ Line 5: / Line 5: @@
 Incremental Learning(IL) is also referred to as: Continual Learning(CL), Life Long Learning(LLL), Online Learning(OL), Never Ending Learning(NEL).
-Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.
+Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.(([[https://arxiv.org/abs/2010.15277|Class-incremental learning: survey and performance evaluation on image classification]]))
 ===== Key Challenge =====
-Catastrophic Forgetting(CF): Learning multiple tasks in sequence, however, remains a substantial challenge for deep learning. When trained on a new task, standard neural networks forget most of the information related to previously learned tasks, a phenomenon referred to as “catastrophic forgetting”((Three scenarios for continual learning)).
+Catastrophic Forgetting(CF): Learning multiple tasks in sequence, however, remains a substantial challenge for deep learning. When trained on a new task, standard neural networks forget most of the information related to previously learned tasks, a phenomenon referred to as “catastrophic forgetting”(([[https://arxiv.org/abs/1904.07734v1|Three scenarios for continual learning]])).
+===== Scenarios =====
+There are 3 scenarios on Incremental Learning(([[https://arxiv.org/abs/1904.07734v1|Three scenarios for continual learning]])), including:
+  * Task Incremental Learning
+  * Domain Incremental Learning
+  * Class Incremental Learning
+===== Methods =====
+All methods of incremental learning can be concluded into 4 categories(([[https://arxiv.org/abs/1904.07734v1|Three scenarios for continual learning]])).
+  * Task specific components(sub-network per task): **XDG**(Context-dependent Gating)
+  * regularized optimization(differently regularizing parameters): **[[deep_learning:incremental_learning:EWC]]**(Elastic Weight Consolidation), **SI**(Synaptic Intelligence)
+  * Modifying Training Data(pseudo-data, generate samples): **[[deep_learning:incremental_learning:LwF]]**(Learning without Forgetting), **DGR**(Deep Generative Replay)
+  * Using Exemplars(store data from previous tasks): **[[deep_learning:incremental_learning:iCaRL]]**
 ===== Resources =====

知识库｜湖南麓川信息科技有限公司

User Tools

Site Tools

Differences

Page Tools