Tongliang Liu

Domain Generalization via Conditional Invariant Representation

2018 · arXiv (Cornell University)

Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: training data and test data have different distributions, and the training …

Adversarial Examples for Hamming Space Search

2018 · IEEE Transactions on Cybernetics

Due to its strong representation learning ability and its facilitation of joint learning for representation and hash codes, deep learning-to-hash has achieved promising results and is becoming increasingly popular for the large-scale approximate nearest neighbor …

Trustable Co-Label Learning From Multiple Noisy Annotators

2021 · IEEE Transactions on Multimedia

Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multiple noisy annotators. Numerous earlier works assume that all labels are noisy, …

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning

2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different label noise types, ranging from class-conditional to instance-dependent noises. In this …

A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation

2025 · arXiv (Cornell University)

Malicious applications of visual manipulation have raised serious threats to the security and reputation of users in many fields. To alleviate these issues, adversarial noise-based defenses have been enthusiastically studied in recent years. However, ``data-only" …