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Found 7 papers, 3 papers with code
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
To continually adapt the prototypes without keeping any prior task data, we propose a novel distillation loss that constrains class prototypes to maintain relative similarities as compared to new task data.
Tackling Online One-Class Incremental Learning by Removing Negative Contrasts
Recent work studies the supervised online continual learning setting where a learner receives a stream of data whose class distribution changes over time.
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning
Continual Learning research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones.
Towards Shape Biased Unsupervised Representation Learning for Domain Generalization
In this work, we propose a learning framework to improve the shape bias property of self-supervised methods.
Ranked #37 on
Domain Generalization
on PACS
Diminishing the Effect of Adversarial Perturbations via Refining Feature Representation
Our method can be applied to any layer of any arbitrary model without the need of any modification or additional training.
