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Found 6 papers, 4 papers with code
An Introduction to Lifelong Supervised Learning
Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i. i. d to the training domains.
IIRC: Incremental Implicitly-Refined Classification
We develop a standardized benchmark that enables evaluating models on the IIRC setup.
Chaotic Continual Learning
Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs.
PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks
Our approach improves the robustness of CNN models against the manifold intrusion problem that may occur in other state-of-the-art mixing approaches.
Adversarial Feature Desensitization
Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs.
