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Found 3 papers, 2 papers with code
Rethinking Deep Contrastive Learning with Embedding Memory
Pair-wise loss functions have been extensively studied and shown to continuously improve the performance of deep metric learning (DML).
Cross-Batch Memory for Embedding Learning
This suggests that the features of instances computed at preceding iterations can be used to considerably approximate their features extracted by the current model.
Ranked #2 on
Image Retrieval
on In-Shop
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation.
Ranked #1 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
