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Robust compressed sensing using generative models
AjilJalal, LiuLiu, AlexandrosG.Dimakis....
Published date-12/01/2020
We consider estimating a high dimensional signal in $\R^n$ using a sublinear number of linear measurements. In analogy to classical compressed sensing, here we assume a generative model as a …
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
DimitriosMallis, EnriqueSanchez, MatthewBell....
Published date-12/01/2020
Clustering
This paper addresses the problem of unsupervised discovery of object landmarks. We take a different path compared to that of existing works, based on 2 novel perspectives: (1) Self-training: starting …
Disentangling Label Distribution for Long-tailed Visual Recognition
YoungkyuHong, SeungjuHan, KwangheeChoi....
Published date-12/01/2020
The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution. Such protocol …
Bayesian Pseudocoresets
DionysisManousakas, ZuhengXu, CeciliaMascolo....
Published date-12/01/2020
BayesianInference
Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data. Recent work has found that a small, weighted subset of data (a coreset) may be used …
Soft Contrastive Learning for Visual Localization
JanineThoma, DandaPaniPaudel, LucV.Gool....
Published date-12/01/2020
ContrastiveLearning, ImageRetrieval, VisualLocalization
Localization by image retrieval is inexpensive and scalable due to simple mapping and matching techniques. Such localization, however, depends upon the quality of image features often obtained using Contrastive learning …
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
GiuliaDenevi, MassimilianoPontil, CarloCiliberto....
Published date-12/01/2020
Meta-Learning
Biased regularization and fine tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks’ target vectors are all …