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Found 4 papers, 2 papers with code
Deep Clustering via Distribution Learning
Thus, in this work, we provide a theoretical analysis to guide the optimization of clustering via distribution learning.
Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks
Since high-frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.
Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module
On this foundation, we present a novel Differentiable Arithmetic Distribution Module (DADM), which is designed to extract the intrinsic probability distributions from images.
Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks
Since high frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.
