DOST -- Domain Obedient Self-supervised Training for Multi Label Classification with Noisy Labels

The enormous demand for annotated data brought forth by deep learning techniques has been accompanied by the problem of annotation noise. Although this issue has been widely discussed in machine learning literature, it has been relatively unexplored in the context of "multi-label classification" (MLC) tasks which feature more complicated kinds of noise. Additionally, when the domain in question has certain logical constraints, noisy annotations often exacerbate their violations, making such a system unacceptable to an expert. This paper studies the effect of label noise on domain rule violation incidents in the MLC task, and incorporates domain rules into our learning algorithm to mitigate the effect of noise. We propose the Domain Obedient Self-supervised Training (DOST) paradigm which not only makes deep learning models more aligned to domain rules, but also improves learning performance in key metrics and minimizes the effect of annotation noise. This novel approach uses domain guidance to detect offending annotations and deter rule-violating predictions in a self-supervised manner, thus making it more "data efficient" and domain compliant. Empirical studies, performed over two large scale multi-label classification datasets, demonstrate that our method results in improvement across the board, and often entirely counteracts the effect of noise.

SRDCNN: Strongly Regularized Deep Convolution Neural Network Architecture for Time-series Sensor Signal Classification Tasks

Deep Neural Networks (DNN) have been successfully used to perform classification and regression tasks, particularly in computer vision based applications. Recently, owing to the widespread deployment of Internet of Things (IoT), we identify that the classification tasks for time series data, specifically from different sensors are of utmost importance. In this paper, we present SRDCNN: Strongly Regularized Deep Convolution Neural Network (DCNN) based deep architecture to perform time series classification tasks. The novelty of the proposed approach is that the network weights are regularized by both L1 and L2 norm penalties. Both of the regularization approaches jointly address the practical issues of smaller number of training instances, requirement of quicker training process, avoiding overfitting problem by incorporating sparsification of weight vectors as well as through controlling of weight values. We compare the proposed method (SRDCNN) with relevant state-of-the-art algorithms including different DNNs using publicly available time series classification benchmark (the UCR/UEA archive) time series datasets and demonstrate that the proposed method provides superior performance. We feel that SRDCNN warrants better generalization capability to the deep architecture by profoundly controlling the network parameters to combat the training instance insufficiency problem of real-life time series sensor signals.