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Found 4 papers, 2 papers with code
Entropy-Based Feature Extraction For Real-Time Semantic Segmentation
Patches with high entropy are being processed by the encoder with the largest number of parameters, patches with moderate entropy are processed by the encoder with a moderate number of parameters, and patches with low entropy are processed by the smallest encoder.
Gradient Variance Loss for Structure-Enhanced Image Super-Resolution
Further, we minimize the distance between the computed variance maps to enforce the model to produce high variance gradient maps that will lead to the generation of high-resolution images with sharper edges.
Bias Loss for Mobile Neural Networks
In compact CNNs, due to the limited number of parameters, abundant features are unlikely to be obtained, and feature diversity becomes an essential characteristic.
Ranked #816 on
Image Classification
on ImageNet
Learned Gradient Compression for Distributed Deep Learning
In contrast, we advocate that the gradients across the nodes are correlated and propose methods to leverage this inter-node redundancy to improve compression efficiency.
