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Found 10 papers, 3 papers with code
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.
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 #51 on
Domain Generalization
on PACS
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.
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning
Continual Learning research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks.
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.
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Class prototypes are evolved continually in the same latent space, enabling learning and prediction at any point.
DAS: A Deformable Attention to Capture Salient Information in CNNs
Our claim is that DAS's ability to pay increased attention to relevant features results in performance improvements when added to popular CNNs for Image Classification and Object Detection.
Ranked #1 on
Object Detection
on MSCOCO
DFML: Decentralized Federated Mutual Learning
In the realm of real-world devices, centralized servers in Federated Learning (FL) present challenges including communication bottlenecks and susceptibility to a single point of failure.
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?
Parameter-efficient fine-tuning stands as the standard for efficiently fine-tuning large language and vision models on downstream tasks.
