Fumin Shen

Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing

2021 · arXiv (Cornell University)

Due to its powerful capability of representation learning and high-efficiency computation, deep hashing has made significant progress in large-scale image retrieval. However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure …

Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric

2024 · arXiv (Cornell University)

Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature predominantly focuses on pair-based and proxy-based methods to maximize inter-class discrepancy and minimize …

Discrete Collaborative Filtering

2016

We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. …