Contrastive
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A Simple Framework for Contrastive Learning of Visual ...
proceedings.mlr.press• A contrastive loss function defined for a contrastive pre-diction task. Given a set fx~ kgincluding a positive pair of examples x~ iand x~ j, the contrastive prediction task aims to identify x~ jin fx~ kg k6=ifor a given x~ i. We randomly sample a minibatch of Nexamples and define the contrastive prediction task on pairs of augmented exam-
Understanding Contrastive Representation Learning through ...
proceedings.mlr.pressThe term contrastive loss has also been generally used to refer to various objectives based on positive and negative samples, e.g., in Siamese networks (Chopra et al., 2005; Hadsell et al., 2006). In this work, we focus on the spe-cific form in Equation (1) that is widely used in modern unsupervised contrastive representation learning literature.
Dense Contrastive Learning for Self-Supervised Visual Pre ...
openaccess.thecvf.comcontrastive loss, which extends the conventional InfoNCE loss [29] to a dense paradigm. With the above approaches, we perform contrastive learning densely using a fully con-volutional network (FCN) [26], similar to target dense pre-diction tasks. Our main contributions are thus summarized as follows.
DetCo: Unsupervised Contrastive Learning for Object Detection
openaccess.thecvf.comcontrastive learning [5,19,5,3,18] currently achieved state-of-the-art performance, arousing extensive attention from researchers. Unlike generative methods, contrastive learning avoids the computation-consuming generation step by pulling representations of different views of the same im-age (i.e., positive pairs) close, and pushing representations
Exploring Simple Siamese Representation Learning
arxiv.orgContrastive learning. The core idea of contrastive learn-ing [16] is to attract the positive sample pairs and repulse the negative sample pairs. This methodology has been recently popularized for un-/self-supervised representation learning [36,30,20,37,21,2,35,17,29,8,9]. Simple and effective instantiations of contrastive learning have been ...
Bootstrap Your Own Latent A New Approach to Self ...
arxiv.orgContrastive approaches avoid a costly generation step in pixel space by bringing representation of different views of the same image closer (‘positive pairs’), and spreading representations of views from different images (‘negative pairs’) apart [39, 40]. Contrastive methods often require
Mother-Tongue Interference in the Acquisition of English ...
files.eric.ed.govContrastive analysis is concerned with the study of a pair of languages with the aim of discovering their structural similarities and differences. Contrastive Analysis is a method that was widely used in the 1960s and early 1970s to explain why some features of a target language were more difficult to learn than others. (Mozlan, 2015)
Supervised Contrastive Learning - NIPS
papers.nips.cccontrastive learning which uses only a single positive). These positives are drawn from samples of the same class as the anchor, rather than being data augmentations of the anchor, as done in self-supervised learning. While this is a simple extension to the self-supervised setup, it is non-
Self-Prediction and Contrastive Learning
neurips.ccContrastive Learning: Inter-Sample Classification Given both similar (“positive”) and dissimilar (“negative”) candidates, to identify which ones are similar to the anchor data point is a classification task. There are creative ways to construct a set of data point candidates: 1. The original input and its distorted version
Dimensionality Reduction by Learning an Invariant Mapping
yann.lecun.comA contrastive loss function is employed to learn the param-eters W of a parameterizedfunction GW, in such a way that neighborsare pulled togetherand non-neighborsare pushed apart. Priorknowledgecan beused to identifythe neighbors for each training data point. The method uses an energy based model that uses the