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Found 5 papers, 2 papers with code
Rethinking Efficacy of Softmax for Lightweight Non-Local Neural Networks
Attention maps normalized with softmax operation highly rely upon magnitude of key vectors, and performance is degenerated if the magnitude information is removed.
Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution
Blind super-resolution (blind-SR) is an important task in the field of computer vision and has various applications in real-world.
Ranked #4 on
Blind Super-Resolution
on DIV2KRK - 2x upscaling
Improving Generalization of Batch Whitening by Convolutional Unit Optimization
Batch Whitening is a technique that accelerates and stabilizes training by transforming input features to have a zero mean (Centering) and a unit variance (Scaling), and by removing linear correlation between channels (Decorrelation).
An Ensemble Approach toward Automated Variable Selection for Network Anomaly Detection
While variable selection is essential to optimize the learning complexity by prioritizing features, automating the selection process is preferred since it requires laborious efforts with intensive analysis otherwise.
Mimicking Ensemble Learning with Deep Branched Networks
This paper proposes a branched residual network for image classification.
