Bayesian DivideMix++ for Enhanced Learning with Noisy Labels.

Leveraging inexpensive and human intervention-based annotating methodologies, such as crowdsourcing and web crawling, often leads to datasets with noisy labels. Noisy labels can have a detrimental impact on the performance and generalization of deep neural networks. Robust models that are able to handle and mitigate the effect of these noisy labels are thus essential. In this work, we explore the open challenges of neural network memorization and uncertainty in creating robust learning algorithms with noisy labels. To overcome them, we propose a novel framework called "Bayesian DivideMix++" with two critical components: (i) DivideMix++, to enhance the robustness against memorization and (ii) Monte-Carlo MixMatch, which focuses on improving the effectiveness towards label uncertainty. DivideMix++ improves the pipeline by integrating the warm-up and augmentation pipeline with self-supervised pre-training and dedicated different data augmentations for loss analysis and backpropagation. Monte-Carlo MixMatch leverages uncertainty measurements to mitigate the influence of uncertain samples by reducing their weight in the data augmentation MixMatch step. We validate our proposed pipeline using four datasets encompassing various synthetic and real-world noise settings. We demonstrate the effectiveness and merits of our proposed pipeline using extensive experiments. Bayesian DivideMix++ outperforms the state-of-the-art models by considerable differences in all experiments. Our findings underscore the potential of leveraging these modifications to enhance the performance and generalization of deep neural networks in practical scenarios.

Uncertainty-aware selecting for an ensemble of deep food recognition models.

Deep learning is a machine learning technique that has revolutionized the research community due to its impressive results on various real-life problems. Recently, ensembles of Convolutional Neural Networks (CNN) have proven to achieve high robustness and accuracy in numerous computer vision challenges. As expected, the more models we add to the ensemble, the better performance we can obtain, but, in contrast, more computer resources are needed. Hence, the importance of deciding how many models to use and which models to select from a pool of trained models is huge. From the latter, a common strategy in deep learning is to select the models randomly or according to the results on the validation set. However, in this way models are chosen based on individual performance ignoring how they are expected to work together. Alternatively, to ensure a better complement between models, an exhaustive search can be used by evaluating the performance of several ensemble models based on different numbers and combinations of trained models. Nevertheless, this may result in being high computationally expensive. Considering that epistemic uncertainty analysis has recently been successfully employed to understand model learning, we aim to analyze whether an uncertainty-aware epistemic method can help us decide which groups of CNN models may work best. The method was validated on several food datasets and with different CNN architectures. In most cases, our proposal outperforms the results by a statistically significant range with respect to the baseline techniques and is much less computationally expensive compared to the brute-force search.