
Does Continual Learning = Catastrophic Forgetting?
Continual learning is known for suffering from catastrophic forgetting, ...
read it

Disentanglebased Continual Graph Representation Learning
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a ...
read it

Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student Networks
When a computational system continuously learns from an everchanging en...
read it

Explaining How Deep Neural Networks Forget by Deep Visualization
Explaining the behaviors of deep neural networks, usually considered as ...
read it

Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization
Interpreting the behaviors of Deep Neural Networks (usually considered a...
read it

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Continual learning (CL) is a setting in which an agent has to learn from...
read it

Continual State Representation Learning for Reinforcement Learning using Generative Replay
We consider the problem of building a state representation model in a co...
read it
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structureagnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structurepreserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments.
READ FULL TEXT
Comments
There are no comments yet.