116 citations · 1.1k across the 96 of their papers we have counts for
8 papers · 1 filter
Stain-invariant self supervised learning for histopathology image analysis
Alexandre Tiard, Alex Wong, David Joon Ho +5
We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin (H&E) stained images of breast cancer. Our method is robust to stain variations…
Not Just Streaks: Towards Ground Truth for Single Image Deraining
Yunhao Ba, Howard Zhang, Ethan Yang +8
We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As th…
Graph Spectral Embedding using the Geodesic Betweeness Centrality
Shay Deutsch, Stefano Soatto
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvest…
Gacs-Korner Common Information Variational Autoencoder
Michael Kleinman, Alessandro Achille, Stefano Soatto +1
We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique t…
Towards Differential Relational Privacy and its use in Question Answering
Simone Bombari, Alessandro Achille, Zijian Wang +6
Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) t…
Omni-DETR: Omni-Supervised Object Detection with Transformers
Pei Wang, Zhaowei Cai, Hao Yang +4
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for ob…
Mixed Differential Privacy in Computer Vision
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang +3
We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language…
Contrastive Neighborhood Alignment
Pengkai Zhu, Zhaowei Cai, Yuanjun Xiong +4
We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features whereby data points that are mapped to nearby represe…