Search Results for author:
Found 5 papers, 3 papers with code
Rethinking cluster-conditioned diffusion models
We present a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments.
Exploring the Limits of Deep Image Clustering using Pretrained Models
We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors.
Ranked #1 on
Image Clustering
on CIFAR-10
(using extra training data)
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.
Learning to Detect Adversarial Examples Based on Class Scores
Our method is able to detect adversarial examples generated by various attacks, and can be easily adopted to a plethora of deep classification models.
On the Vulnerability of Capsule Networks to Adversarial Attacks
This paper extensively evaluates the vulnerability of capsule networks to different adversarial attacks.
