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Source: [link] ‘This phenomenon,' they explain, ‘termed “compression helps” in the [2021] paper, is justified by the fact that compression can remove noise and disturbing background features, thereby highlighting the main object in an image, which helps DNNs make better prediction.'
Matching Networks: The algorithm computes embeddings using a support set, and one-shot learns by classifying the query data sample based on which support set embedding is closest to the query embedding – source. The embedding functions can be convolutionalneuralnetworks (CNNs). The CLIP model for ZSL shows 64.3%
Source : Hassanat (2011) [13] These approaches obtained impressive results (over 70% word accuracy) for tests performed with classifiers trained on the same speaker they were tested on. One can also think of CTC as similar to a softmax due to converting the raw output of a network (e.g. Online] arXiv: 1710.01288. Hassanat, A.B.A.
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