Learning from Noisy Labels via Discrepant Collaborative Training

Noise is ubiquitous in the world around us. Difficulty inestimating the noise within a dataset makes learning fromsuch a dataset a difficult and challenging task. In this pa-per, we propose a novel and effective learning frameworkin order to alleviate the adverse effects of noise within adataset. Towards this aim, we modify a collaborative train-ing framework to utilize discrepancy constraints betweenrespective feature extractors enabling the learning of dis-tinct, yet discriminative features, pacifying the adverse ef-fects of noise. Empirical results of our proposed algo-rithm, Discrepant Collaborative Training (DCT), achievecompetitive results against several current state-of-the-artalgorithms across MNIST, CIFAR10 and CIFAR100, as wellas large fine-grained image classification datasets such asCUBS-200-2011 and CARS196 for different levels of noise.