arXiv subjects

Arulmurugan Ambikapathi

Publications and source records attributed to Arulmurugan Ambikapathi.

3 recordsLinked to original sources

arXiv2022

Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data

Anomaly detection in multi-variate time series (MVTS) data is a huge challenge as it requires simultaneous representation of long term temporal dependencies and correlations across multiple variables. More often, this is solved by breaking the complexity through modeling one dependency at a time. In this paper, we propose a Time-series Representational Learning through Contrastive Predictive Coding (TRL-CPC) towards anomaly detection in MVTS data. First, we jointly optimize an encoder, an auto-regressor and a non-linear transformation function to effectively learn the representations of the MVTS data sets, for predicting future trends. It must be noted that the context vectors are representative of the observation window in the MTVS. Next, the latent representations for the succeeding instants obtained through non-linear transformations of these context vectors, are contrasted with the latent representations of the encoder for the multi-variables such that the density for the positive pair is maximized. Thus, the TRL-CPC helps to model the temporal dependencies and the correlations of the parameters for a healthy signal pattern. Finally, fitting the latent representations are fit into a Gaussian scoring function to detect anomalies. Evaluation of the proposed TRL-CPC on three MVTS data sets against SOTA anomaly detection methods shows the superiority of TRL-CPC.

cs.LG↗

arXiv2021

Knowledge Capture and Replay for Continual Learning

Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters. Extraction and utilization of encoded knowledge representations are vital when data is no longer available in the future, especially in a continual learning scenario. In this work, we introduce {\em flashcards}, which are visual representations that {\em capture} the encoded knowledge of a network as a recursive function of predefined random image patterns. In a continual learning scenario, flashcards help to prevent catastrophic forgetting and consolidating knowledge of all the previous tasks. Flashcards need to be constructed only before learning the subsequent task, and hence, independent of the number of tasks trained before. We demonstrate the efficacy of flashcards in capturing learned knowledge representation (as an alternative to the original dataset) and empirically validate on a variety of continual learning tasks: reconstruction, denoising, task-incremental learning, and new-instance learning classification, using several heterogeneous benchmark datasets. Experimental evidence indicates that: (i) flashcards as a replay strategy is { \em task agnostic}, (ii) performs better than generative replay, and (iii) is on par with episodic replay without additional memory overhead.

cs.LG↗

arXiv2019

FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision

Regular inspection of rail valves and engines is an important task to ensure the safety and efficiency of railway networks around the globe. Over the past decade, computer vision and pattern recognition based techniques have gained traction for such inspection and defect detection tasks. An automated end-to-end trained system can potentially provide a low-cost, high throughput, and cheap alternative to manual visual inspection of these components. However, such systems require a huge amount of defective images for networks to understand complex defects. In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves. Our approach uses a two-step method to perform high precision image segmentation of rail-valves resulting in pixel-wise accurate segmentation. Thereafter, a computer vision technique is used to identify faulty valves. We demonstrate that the proposed approach results in improved detection performance when compared to current state-of-theart techniques used in fault detection.

cs.CV↗