Search Results for author:
Found 7 papers, 5 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 Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition
In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted.
Multi-view adaptive graph convolutions for graph classification
In this paper, a novel multi-view methodology for graph-based neural networks is proposed.
GANs in computer vision ebook
We do hope that this series will provide you a big overview of the field, so that you will not need to read all the literature by yourself, independent of your background on GANs.
