Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

computer vision and pattern recognition, 2018.

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We present an unsupervised feature learning approach by maximizing distinction between instances via a novel nonparametric softmax formulation

Abstract:

Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similari...More

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