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Found 5 papers, 4 papers with code
Training Unbiased Diffusion Models From Biased Dataset
While directly applying it to score-matching is intractable, we discover that using the time-dependent density ratio both for reweighting and score correction can lead to a tractable form of the objective function to regenerate the unbiased data density.
Label-Noise Robust Diffusion Models
This paper proposes Transition-aware weighted Denoising Score Matching (TDSM) for training conditional diffusion models with noisy labels, which is the first study in the line of diffusion models.
SAAL: Sharpness-Aware Active Learning
Specifically, our proposed method, Sharpness-Aware Active Learning (SAAL), constructs its acquisition function by selecting unlabeled instances whose perturbed loss becomes maximum.
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
In sample generation, we add an auxiliary term to the pre-trained score to deceive the discriminator.
Ranked #1 on
Conditional Image Generation
on CIFAR-10
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection
To break through these observations, we propose a simple solution AltUB which introduces alternating training to update the base distribution of normalizing flow for anomaly detection.
Ranked #2 on
Anomaly Detection
on BTAD
(using extra training data)
