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UNSUPERVISED, SEMI-SUPERVISED, AND SUPERVISED LEARNING USING DEEP LEARNING BASED PROBABILISTIC GENERATIVE MODELS
UNSUPERVISED, SEMI-SUPERVISED, AND SUPERVISED LEARNING USING DEEP LEARNING BASED PROBABILISTIC GENERATIVE MODELS
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机译：使用深受基于深度学习的概率生成模型的无监督，半监督和监督学习
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摘要
Embodiments of the present systems and methods may provide techniques to discover features such as object categories that provide improved accuracy and performance. For example, in an embodiment, a method may comprise extracting, at the computer system, features from a dataset comprising a plurality of data samples using a backbone neural network to form a features vector for each data sample, training, at the computer system, using the features vectors for at least some of the plurality of data samples, an unsupervised generative probabilistic model to perform clustering of extracted features of the at least some of the plurality of data samples by minimizing a negative Log-Likelihood function, wherein clusters of extracted features form categories, and categorizing, at the computer system, at least some different data samples of the plurality of data samples, into the formed categories.
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