Jingyang Zhang

Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure.

2021 · arXiv (Cornell University)

Enabling out-of-distribution (OOD) detection for DNNs is critical for their safe and reliable operation in the open world. Unfortunately, current works in both methodology and evaluation focus on rather contrived detection problems, and only consider …

Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments

2023 · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Many real-world scenarios in which DNN-based recognition systems are deployed have inherently fine-grained attributes (e.g., bird-species recognition, medical image classification). In addition to achieving reliable accuracy, a critical subtask for these models is to detect …

Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models

2024 · arXiv (Cornell University)

The problem of pre-training data detection for large language models (LLMs) has received growing attention due to its implications in critical issues like copyright violation and test data contamination. Despite improved performance, existing methods (including …

Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

2024 · Tsinghua Science & Technology

Federated learning is an emerging privacy-preserving distributed learning paradigm, in which many clients collaboratively train a shared global model under the orchestration of a remote server. Most current works on federated learning have focused on …