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Jiafeng Mao

4 accepted papers

2026

Difficulty Controlled Diffusion Model for Synthesizing Effective Training Data

AAAI 2026technical

Generative models have become a powerful tool for synthesizing training data in computer vision tasks. Current approaches solely focus on aligning generated images with the target dataset distribution. As a result, they capture only the common features in the real dataset and mostly generate "easy s

Cited by 0SourcePDFScholar
2024

Dealing with Synthetic Data Contamination in Online Continual Learning

NeurIPS 2024poster

Image generation has shown remarkable results in generating high-fidelity realistic images, in particular with the advancement of diffusion-based models. However, the prevalence of AI-generated images may have side effects for the machine learning community that are not clearly identified. Meanwhile…

2024

SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning

ECCV 2024poster

"Open-set semi-supervised learning (OSSL) leverages practical open-set unlabeled data, comprising both in-distribution (ID) samples from seen classes and out-of-distribution (OOD) samples from unseen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision bound…