https://www.selleckchem.com/btk.html
Experiments conducted on three real-world benchmarks, demonstrating CAN performs favorably against previous state-of-the-arts.Transformation Equivariant Representations (TERs) aim to capture the intrinsic visual structures that equivary to various transformations by expanding the notion of translation equivariance underlying the success of Convolutional Neural Networks (CNNs). For this purpose, we present both deterministic AutoEncoding Transformations (AET) and probabilistic AutoEncoding Variational Transformations (AVT) models to learn visual repr
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