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20142023
most citedUnsupervised Feature Learning via Non-Parametric Instance-level Discrimination

174 citations · 711 across the 57 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

cs.CV2020★ 5 cited

Unsupervised deep learning for grading of age-related macular degeneration using retinal fundus images

Baladitya Yellapragada, Sascha Hornhauer, Kiersten Snyder +2

Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-…

cs.CV2020★ 30 cited

Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Xudong Wang, Long Lian, Zhongqi Miao +2

Natural data are often long-tail distributed over semantic classes. Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data, th…

cs.CV2020

Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters

Xudong Wang, Stella X. Yu

Convolution is the main building block of convolutional neural networks (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels increa…

cs.CV2020★ 13 cited

Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination

Xudong Wang, Ziwei Liu, Stella X. Yu

Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced data…

cs.LG2020★ 1 cited

C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning

Yifei Xing, Rudrasis Chakraborty, Minxuan Duan +1

The James-Stein (JS) shrinkage estimator is a biased estimator that captures the mean of Gaussian random vectors.While it has a desirable statistical property of dominance over the…

cs.CV2020★ 5 cited

BatVision with GCC-PHAT Features for Better Sound to Vision Predictions

Jesper Haahr Christensen, Sascha Hornauer, Stella Yu

Inspired by sophisticated echolocation abilities found in nature, we train a generative adversarial network to predict plausible depth maps and grayscale layouts from sound. To ach…

cs.CV2020★ 6 cited

3D Shape Reconstruction from Free-Hand Sketches

Jiayun Wang, Jierui Lin, Qian Yu +3

Sketches are the most abstract 2D representations of real-world objects. Although a sketch usually has geometrical distortion and lacks visual cues, humans can effortlessly envisio…