Simple Siamese Representation Learning
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Dimensionality Reduction by Learning an Invariant Mapping
yann.lecun.comis not constrained by simple distance measures in the input space. The learning architecture is somewhat similar to the one discussed in [4, 5]. Section 2 describes the generalframework,the loss func-tion, and draws an analogy with a mechanical spring sys-tem. The ideas in this section are made concrete in sec-tion3.
An Empirical Study of Training Self-Supervised Vision ...
arxiv.orgContrastive learning is commonly instantiated as some forms of Siamese networks [3]. Recently, a series of works [18,7,13] retain the Siamese architectures but eliminate the requirement of negative samples. The success of these methods suggest that it is of central importance to learn in-variant features by matching positive samples. Transformers.
DeepFace: Closing the Gap to Human-Level Performance in ...
www.cs.toronto.educompact face representation, in sheer contrast to the shift toward tens of thousands of appearance features in other re-cent systems [5,7,2]. The proposed system differs from the majority of con-tributions in the field in that it uses the deep learning (DL) framework [3,21] in lieu of well engineered features. DL is