Shikun Li

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, …

Model Conversion via Differentially Private Data-Free Distillation

2023 · arXiv (Cornell University)

While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment which leads to privacy leakage of training data. In this work, …