1 citations · 2 across the 6 of their papers we have counts for
6 papers
An Iteration-Free Fixed-Point Estimator for Diffusion Inversion
Yifei Chen, Kaiyu Song, Yan Pan +3
Diffusion inversion aims to recover the initial noise corresponding to a given image such that this noise can reconstruct the original image through the denoising diffusion process…
GAFD-CC: Global-Aware Feature Decoupling with Confidence Calibration for OOD Detection
Kun Zou, Yongheng Xu, Jianxing Yu +3
Out-of-distribution (OOD) detection is paramount to ensuring the reliability and robustness of learning models in real-world applications. Existing post-hoc OOD detection methods d…
Flow Matching Posterior Sampling: A Training-free Conditional Generation for Flow Matching
Kaiyu Song, Hanjiang Lai, Yan Pan +2
Training-free conditional generation based on flow matching aims to leverage pre-trained unconditional flow matching models to perform conditional generation without retraining. Re…
MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model
Kaiyu Song, Hanjiang Lai
Deep neural networks (DNNs) are vulnerable to adversarial perturbation, where an imperceptible perturbation is added to the image that can fool the DNNs. Diffusion-based adversaria…
Two Simple Principles for Diffusion-Based Test-Time Adaptation
Kaiyu Song, Hanjiang Lai, Yan Pan +2
Recently, diffusion-based test-time adaptations (TTA) have shown great advances, which leverage a diffusion model to map the images in the unknown test domain to the training domai…
Improving Entropy-Based Test-Time Adaptation from a Clustering View
Guoliang Lin, Hanjiang Lai, Yan Pan +1
Domain shift is a common problem in the realistic world, where training data and test data follow different data distributions. To deal with this problem, fully test-time adaptatio…