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Found 10 papers, 1 papers with code
Channel Self-Supervision for Online Knowledge Distillation
Recently, researchers have shown an increased interest in the online knowledge distillation.
Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency
In this paper, we observe that the reduced image retains relatively complete shape semantics but loses extensive texture information.
Feature Mining: A Novel Training Strategy for Convolutional Neural Network
In this paper, we propose a novel training strategy for convolutional neural network(CNN) named Feature Mining, that aims to strengthen the network's learning of the local feature.
Go Small and Similar: A Simple Output Decay Brings Better Performance
Regularization and data augmentation methods have been widely used and become increasingly indispensable in deep learning training.
Self-supervision of Feature Transformation for Further Improving Supervised Learning
In this paper we find that features in CNNs can be also used for self-supervision.
Self-supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning
In this paper, we show that feature transformations within CNNs can also be regarded as supervisory signals to construct the self-supervised task, called \emph{internal pretext task}.
White Paper Assistance: A Step Forward Beyond the Shortcut Learning
The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested.
FocusedDropout for Convolutional Neural Network
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially.
Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions
In this paper, we propose Selective Output Smoothing Regularization, a novel regularization method for training the Convolutional Neural Networks (CNNs).
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network
In this paper, we propose a novel data augmentation strategy named Cut-Thumbnail, that aims to improve the shape bias of the network.
