41 citations · 44 across the 20 of their papers we have counts for
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Reward Incremental Learning in Text-to-Image Generation
Maorong Wang, Jiafeng Mao, Xueting Wang +1
The recent success of denoising diffusion models has significantly advanced text-to-image generation. While these large-scale pretrained models show excellent performance in genera…
Dealing with Synthetic Data Contamination in Online Continual Learning
Maorong Wang, Nicolas Michel, Jiafeng Mao +1
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…
Difficulty Controlled Diffusion Model for Synthesizing Effective Training Data
Zerun Wang, Jiafeng Mao, Xueting Wang +1
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…
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
Zerun Wang, Liuyu Xiang, Lang Huang +3
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) s…
Training-Free Sketch-Guided Diffusion with Latent Optimization
Sandra Zhang Ding, Jiafeng Mao, Kiyoharu Aizawa
Based on recent advanced diffusion models, Text-to-image (T2I) generation models have demonstrated their capabilities to generate diverse and high-quality images. However, leveragi…
From Obstacles to Resources: Semi-supervised Learning Faces Synthetic Data Contamination
Zerun Wang, Jiafeng Mao, Liuyu Xiang +1
Semi-supervised learning (SSL) can improve model performance by leveraging unlabeled images, which can be collected from public image sources with low costs. In recent years, synth…