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Rethinking cluster-conditioned diffusion models
We present a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments.
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection
Intriguingly, we show that (i) PLP outperforms the previous state-of-the-art \citep{ming2022mcm} on all $5$ large-scale benchmarks based on ImageNet, specifically by an average AUROC gain of 3. 4\% using the largest CLIP model (ViT-G), (ii) we show that linear probing outperforms fine-tuning by large margins for CLIP architectures (i. e.
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