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Found 3 papers, 1 papers with code
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs.
One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code
Moreover, our model supports self-supervised pretraining with the same sparsely activated way, resulting in better initialized parameters for different modalities.
Semantically Proportional Patchmix for Few-Shot Learning
Few-shot learning aims to classify unseen classes with only a limited number of labeled data.
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