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Found 6 papers, 5 papers with code
Faster ISNet for Background Bias Mitigation on Deep Neural Networks
Representing a potentially massive training speed improvement over ISNet, the proposed architectures introduce LRP optimization into a gamut of applications that the original model cannot feasibly handle.
Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization
Features in images' backgrounds can spuriously correlate with the images' classes, representing background bias.
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces
Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths.
COVID-19 detection using chest X-rays: is lung segmentation important for generalization?
Purpose: we evaluated the generalization capability of deep neural networks (DNNs), trained to classify chest X-rays as Covid-19, normal or pneumonia, using a relatively small and mixed dataset.
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification
The presented methodology surpassed performances obtained with FBCCA and SVMs (more traditional SSVEP classification methods) in BCIs with small data lengths and one electrode.
A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays
Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal.
