UnbiasedNets: a dataset diversification framework for robustness bias alleviation in neural networks

Abstract

Performance of trained neural network (NN) models, in terms of testing accuracy, has improved remarkably over the past several years, especially with the advent of deep learning. However, even the most accurate NNs can be biased toward a specific output classification due to the inherent bias in the available training datasets, which may propagate to the real-world implementations. This paper deals with the robustness bias, i.e., the bias exhibited by the trained NN by having a significantly large robustness to noise for a certain output class, as compared to the remaining output classes. The bias is shown to result from imbalanced datasets, i.e., the datasets where all output classes are not equally represented. Towards this, we propose the UnbiasedNets framework, which leverages K-means clustering and the NN’s noise tolerance to diversify the given training dataset, even from relatively smaller datasets. This generates balanced datasets and reduces the bias within the datasets themselves. To the best of our knowledge, this is the first framework catering to the robustness bias problem in NNs. We use real-world datasets to demonstrate the efficacy of the UnbiasedNets for data diversification, in case of both binary and multi-label classifiers. The results are compared to well-known tools aimed at generating balanced datasets, and illustrate how existing works have limited success while addressing the robustness bias. In contrast, UnbiasedNets provides a notable improvement over existing works, while even reducing the robustness bias significantly in some cases, as observed by comparing the NNs trained on the diversified and original datasets.

Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms

“…All data set construction steps for learning were conducted at the level of age-matched participants to create data sets for screening MMD and predicting the stages of MMD; these steps also served to prevent overfitting, information leakage, and the risk of bias. 19 We used retinal photographs from both eyes, as the progression of MMD manifested in both hemispheres, separately. The patients with stages 0 to For the development of the model, we adopted the typical neural network, ResNeXt50.…”