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Training Deep neural networks (DNNs) on noisy labeled datasets is achallenging problem, because learning on mislabeled examples deteriorates theperformance of the network. As the ground truth availability is limited withreal-world noisy datasets, previous papers created synthetic noisy datasets byrandomly modifying the labels of training examples of clean datasets. However,no final conclusions can be derived by just using this random noise, since itexcludes feature-dependent noise. Thus, it is imperative to generatefeature-dependent noisy datasets that additionally provide ground truth.Therefore, we propose an intuitive approach to creating feature-dependent noisydatasets by utilizing the training predictions of DNNs on clean datasets thatalso retain true label information. We refer to these datasets as "Pseudo Noisydatasets". We conduct several experiments to establish that Pseudo noisydatasets resemble feature-dependent noisy datasets across different conditions.We further randomly generate synthetic noisy datasets with the same noisedistribution as that of Pseudo noise (referred as "Randomized Noise") toempirically show that i) learning is easier with feature-dependent label noisecompared to random noise, ii) irrespective of noise distribution, Pseudo noisydatasets mimic feature-dependent label noise and iii) current training methodsare not generalizable to feature-dependent label noise. Therefore, we believethat Pseudo noisy datasets will be quite helpful to study and develop robusttraining methods.

Generation and Analysis of Feature-Dependent Pseudo Noise for Training Deep Neural Networks