[1] Projection Regret: Reducing Background Bias via Diffusion Models for Novelty Detection, NeurIPS 2023
[2] A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks, NeurIPS 2018.[3] Auto-Encoding Variational Bayes, NeurIPS 2014
[4] Novelty Detection and Neural Networks, Ijcnn 1994.
[5] do deep generative models know what they don’t know, ICLR 2019
[6] Novelty Detection via Blurring, ICLR 2020.
[7] Input Complexity and out-of-distribution detection with likelihood-based generative models, ICLR 2020.
[8] Likelihood Ratios for Out-of-distribution detection, NeurIPS 2019.
[9] ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, ICLR 2019.
[10] Out-of-Distribution Generalization in Kernel Regression, NeurIPS 2021.
[11] 80 milion tiny images: a large dataset for nonparameteric object and scene recognition, TPAMI 2008.
[12] Consistency Models, ICML 2023.
[13] SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations, ICLR 2022.
[14] DiffEdit: Diffusion-based semantic image editing with mask guidance, ICLR 2023.
[15] The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, CVPR 2018.
[16] A closer look at Fourier spectrum discrepancies for CNN-generated images detection, CVPR 2019.
[17] On the detection of synthetic images generated by diffusion models, ICASSP 2023.
[18] Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust, NeurIPS 2019.
[19] D. P. Kingma and R. Gao., Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation, NeurIPS 2023.
[20] K. Clark and P. Jaini, Text-to-image Diffusion Models are Zero-Shot Classifiers, NeurIPS 2023.