Search Results for author:
Found 6 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.
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling
Detecting whether examples belong to a given in-distribution or are Out-Of-Distribution (OOD) requires identifying features specific to the in-distribution.
Out of Distribution (OOD) Detection
Self-Supervised Anomaly Detection
+1
A General Framework for Unsupervised Anomaly Detection
In this paper we present GenAD, a simple and generic framework for detecting examples that lie out-of-distribution for a given training set.
Out-of-Distribution Detection
Unsupervised Anomaly Detection
Unsupervised Anomaly Detection From Semantic Similarity Scores
Classifying samples as in-distribution or out-of-distribution (OOD) is a challenging problem of anomaly detection and a strong test of the generalisation power for models of the in-distribution.
