Search Results for author: Shoaib Ahmed Siddiqui

Found 14 papers, 5 papers with code

Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification

1 code implementation13 Jun 2022 Adriano Lucieri, Fabian Schmeisser, Christoph Peter Balada, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed

Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation.

Classification Decision Making +2

Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training

no code implementations3 Mar 2022 Pervaiz Iqbal Khan, Shoaib Ahmed Siddiqui, Imran Razzak, Andreas Dengel, Sheraz Ahmed

The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.

Identifying Layers Susceptible to Adversarial Attacks

no code implementations10 Jul 2021 Shoaib Ahmed Siddiqui, Thomas Breuel

In this paper, we investigate the use of pretraining with adversarial networks, with the objective of discovering the relationship between network depth and robustness.

Dimensionality Reduction

Benchmarking adversarial attacks and defenses for time-series data

no code implementations30 Aug 2020 Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed

This paves the way for future research in the direction of adversarial attacks and defenses, particularly for time-series data.

Adversarial Defense Time Series

Interpreting Deep Models through the Lens of Data

1 code implementation5 May 2020 Dominique Mercier, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed

Identification of input data points relevant for the classifier (i. e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as well as dataset debugging.

TSInsight: A local-global attribution framework for interpretability in time-series data

no code implementations ICLR 2020 Shoaib Ahmed Siddiqui, Dominique Mercier, Andreas Dengel, Sheraz Ahmed

We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty.

Time Series

KINN: Incorporating Expert Knowledge in Neural Networks

no code implementations15 Feb 2019 Muhammad Ali Chattha, Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Ludger van Elst, Andreas Dengel, Sheraz Ahmed

The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare.

Anomaly Detection Management +1

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

3 code implementations19 Dec 2018 Mohsin Munir, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed

In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series.

Time Series Anomaly Detection Unsupervised Anomaly Detection

Deep One-Class Classification

1 code implementation ICML 2018 Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft

Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.

Anomaly Detection Classification +2

TSViz: Demystification of Deep Learning Models for Time-Series Analysis

1 code implementation8 Feb 2018 Shoaib Ahmed Siddiqui, Dominik Mercier, Mohsin Munir, Andreas Dengel, Sheraz Ahmed

This is a step towards making informed/explainable decisions in the domain of time-series, powered by deep learning.

Self-Driving Cars Time Series +1

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