Unsupervised Visual Representation Learning by Graph-based Consistent Constraints

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Xiaofeng Li, Wei-Chih Hung, Jia-Bin Huang, Shengjin Wang, Narendra Ahuja, Ming-Hsuan Yang
Journal/Conference Name ECCV
Paper Category
Paper Abstract 1. We show cycle detection results using the proposed unsupervised constraint mining approach on the large-scale ImageNet 2012 dataset as well as three image datasets with generic objects (CIFAR-10), fine-grained objects (CUB200-2011), and scene classes (MIT indoor-67), respectively. 2. We show examples of easy negative image pairs (i.e., image pairs with large Euclidean distance in the feature space) and hard negative sample pairs (i.e., image pairs with large geodesic distance in the k-NN graph). 3. We show additional qualitative results and a detailed quantitative evaluation for the unsupervised feature learning task. 4. We report detailed quantitative results on three image classification datasets for the semi-supervised learning task.
Date of publication 2016
Code Programming Language MATLAB

Copyright Researcher 2021