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JingHao Zheng

2 accepted papers

2026

Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models

ICLR 2026poster

Synthetically augmenting training datasets with diffusion models has been an effective strategy for improving generalization of image classifiers. However, existing techniques struggle to ensure the diversity of generation and increase the size of the data by up to 10-30x to improve the in-distribut…

Cited by 0SourcecodeScholar
2024

Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement

NeurIPS 2024spotlight

Machine unlearning (MU) has emerged to enhance the privacy and trustworthiness of deep neural networks. Approximate MU is a practical method for large-scale models. Our investigation into approximate MU starts with identifying the steepest descent direction, minimizing the output Kullback-Leibler di…