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Found 4 papers, 1 papers with code
e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce
Understanding vision and language representations of product content is vital for search and recommendation applications in e-commerce.
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning
Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels.
Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty
Label noise is a critical factor that degrades the generalization performance of deep neural networks, thus leading to severe issues in real-world problems.
Ranked #23 on
Image Classification
on Clothing1M
(using extra training data)
Graphs, Entities, and Step Mixture
Existing approaches for graph neural networks commonly suffer from the oversmoothing issue, regardless of how neighborhoods are aggregated.
