Transcription of Unsupervised Feature Learning via Non-Parametric Instance ...
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Unsupervised Feature Learning via Non-Parametric Instance ...
Unsupervised Feature Learning via Non-Parametric Instance DiscriminationZhirong Wu? Yuanjun Xiong Stella X. Yu?Dahua Lin ?UC Berkeley / ICSI Chinese University of Hong Kong Amazon RekognitionAbstractNeural net classifiers trained on data with annotatedclass labels can also capture apparent visual similarityamong categories without being directed to do so. We studywhether this observation can be extended beyond the con-ventional domain of supervised Learning : Can we learn agood Feature representation that captures apparent similar-ity among instances, instead of classes, by merely askingthe Feature to be discriminative of individual instances?We formulate this intuition as a Non-Parametric clas-sification problem at the Instance -level, and use noise-contrastive estimation to tackle the computational chal-lenges imposed by the large number of Instance experimental results demonstrate that, under unsu-pervised Learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large method is also remarkable for consistently improv-ing test performance with more training data and betternetwork architectures.
Self-supervised Learning. Self-supervised learning ex-ploits internal structures of data and formulates predictive tasks to train a model. Specifically, the model needs to pre-dict either an omitted aspect or component of an instance given the …
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