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Found 4 papers, 1 papers with code
Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results.
Complexity Controlled Generative Adversarial Networks
One of the issues faced in training Generative Adversarial Nets (GANs) and their variants is the problem of mode collapse, wherein the training stability in terms of the generative loss increases as more training data is used.
Learning Neural Network Classifiers with Low Model Complexity
Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
Scalable Twin Neural Networks for Classification of Unbalanced Data
In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets.
