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Found 4 papers, 3 papers with code
ShuffleMix: Improving Representations via Channel-Wise Shuffle of Interpolated Hidden States
Inspired by good robustness of alternative dropout strategies against over-fitting on limited patterns of training samples, this paper introduces a novel concept of ShuffleMix -- Shuffle of Mixed hidden features, which can be interpreted as a kind of dropout operation in feature space.
Improving Deep Representation Learning via Auxiliary Learnable Target Coding
Deep representation learning is a subfield of machine learning that focuses on learning meaningful and useful representations of data through deep neural networks.
Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization
Such target coding schemes are less flexible to model inter-class correlation and are sensitive to sparse and imbalanced data distribution as well.
Classification of Single-View Object Point Clouds
By adapting existing ModelNet40 and ScanNet datasets to the single-view, partial setting, experiment results can verify the necessity of object pose estimation and superiority of our PAPNet to existing classifiers.
