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Generalizability is the ultimate goal of Machine Learning (ML) imageclassifiers, for which noise and limited dataset size are among the majorconcerns. We tackle these challenges through utilizing the framework of deepMultitask Learning (dMTL) and incorporating image depth estimation as anauxiliary task. On a customized and depth-augmented derivation of the MNISTdataset, we show a) multitask loss functions are the most effective approach ofimplementing dMTL, b) limited dataset size primarily contributes toclassification inaccuracy, and c) depth estimation is mostly impacted by noise.In order to further validate the results, we manually labeled the NYU Depth V2dataset for scene classification tasks. As a contribution to the field, we havemade the data in python native format publicly available as an open-sourcedataset and provided the scene labels. Our experiments on MNIST andNYU-Depth-V2 show dMTL improves generalizability of the classifiers when thedataset is noisy and the number of examples is limited.

Minimizing the Effect of Noise and Limited Dataset Size in Image  Classification Using Depth Estimation as an Auxiliary Task with Deep  Multitask Learning