4 datasets found

The paper proposes a technique for domain adaptation in stacked autoencoders using systematic dropout.

The dataset is used for deep hashing network for unsupervised domain adaptation.

This paper presents DADLNet, a dynamic domain adaptation based deep learning network framework for decoding motor imagery tasks.

A semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation.