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Found 3 papers, 1 papers with code
Towards Better Adversarial Purification via Adversarial Denoising Diffusion Training
Empirical results show that ADDT improves the robustness of DBP models.
DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Positive-Negative Prompt-Tuning
Although recent attempts have employed fine-tuning or prompt-tuning strategies to teach the pre-trained diffusion model novel concepts from a reference image set, they have the drawback of overfitting to the given reference images, particularly in one-shot applications, which is harmful to generate diverse and high-quality images while maintaining generation controllability.
Adversarially-Aware Robust Object Detector
In this work, we empirically explore the model training for adversarial robustness in object detection, which greatly attributes to the conflict between learning clean images and adversarial images.
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