  The development of deep learning based image representation learning (IRL)
methods has attracted great attention for various image understanding problems.
Most of these methods require the availability of a high quantity and quality
of annotated training images, which can be time-consuming and costly to gather.
To reduce labeling costs, crowdsourced data, automatic labeling procedures or
citizen science projects can be considered. However, such approaches increase
the risk of including label noise in training data. It may result in
overfitting on noisy labels when discriminative reasoning is employed. This
leads to sub-optimal learning procedures, and thus inaccurate characterization
of images. To address this, we introduce a generative reasoning integrated
label noise robust deep representation learning (GRID) approach. Our approach
aims to model the complementary characteristics of discriminative and
generative reasoning for IRL under noisy labels. To this end, we first
integrate generative reasoning into discriminative reasoning through a
supervised variational autoencoder. This allows GRID to automatically detect
training samples with noisy labels. Then, through our label noise robust hybrid
representation learning strategy, GRID adjusts the whole learning procedure for
IRL of these samples through generative reasoning and that of other samples
through discriminative reasoning. Our approach learns discriminative image
representations while preventing interference of noisy labels independently
from the IRL method being selected. Thus, unlike the existing methods, GRID
does not depend on the type of annotation, neural network architecture, loss
function or learning task, and thus can be directly utilized for various
problems. Experimental results show its effectiveness compared to
state-of-the-art methods. The code of GRID is publicly available at
https://github.com/gencersumbul/GRID.
