Search Results for author: Sheraz Ahmed

Found 61 papers, 20 papers with code

DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification

no code implementations4 Jul 2024 Saifullah Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed

In this paper, we aim to bridge this research gap by introducing DocXplain, a novel model-agnostic explainability method specifically designed for generating high interpretability feature attribution maps for the task of document image classification.

Document Image Classification Fairness +1

VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting

no code implementations7 May 2024 Alireza Koochali, Ensiye Tahaei, Andreas Dengel, Sheraz Ahmed

This paper presents VAEneu, an innovative autoregressive method for multistep ahead univariate probabilistic time series forecasting.

Probabilistic Time Series Forecasting Time Series

Class Conditional Time Series Generation with Structured Noise Space GAN

no code implementations20 Dec 2023 Hamidreza Gholamrezaei, Alireza Koochali, Andreas Dengel, Sheraz Ahmed

This paper introduces Structured Noise Space GAN (SNS-GAN), a novel approach in the field of generative modeling specifically tailored for class-conditional generation in both image and time series data.

Time Series Time Series Generation

Medi-CAT: Contrastive Adversarial Training for Medical Image Classification

no code implementations31 Oct 2023 Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed

This paper proposes a training strategy Medi-CAT to overcome the underfitting and overfitting phenomena in medical imaging datasets.

Contrastive Learning Image Classification +1

A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content

no code implementations29 Oct 2023 Pervaiz Iqbal Khan, Muhammad Nabeel Asim, Andreas Dengel, Sheraz Ahmed

Following the need for an optimal language model competent in extracting useful patterns from social media text, the key goal of this paper is to train language models in such a way that they learn to derive generalized patterns.

Contrastive Learning Language Modelling

PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction

no code implementations5 Oct 2023 Saifullah Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed

We conduct a comprehensive evaluation of the algorithm across various client and privacy settings, and demonstrate its capability to achieve comparable performance and privacy guarantees to standalone DP, even when accommodating an increasing number of participating clients.

Document AI Federated Learning +1

Segment Anything for Microscopy

1 code implementation bioRxiv 2023 Anwai Archit, Sushmita Nair, Nabeel Khalid, Paul Hilt, Vikas Rajashekar, Marei Freitag, Sagnik Gupta, Andreas Dengel, Sheraz Ahmed, Constantin Pape

We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data.

Image Segmentation Instance Segmentation +3

From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification

no code implementations28 Mar 2023 Dominique Mercier, Andreas Dengel, Sheraz Ahmed

In this work, two very prominent GAN-based architectures were evaluated in the context of private time series classification.

Benchmarking Privacy Preserving +3

Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids

no code implementations23 Sep 2022 Christoph Balada, Max Bondorf, Sheraz Ahmed, Andreas Dengela, Markus Zdrallek

By publishing the first large-scale real-world dataset, we aim to shed light on the previously largely unrecognized potential of PLC data and emphasize machine-learning-based research in low-voltage distribution networks by presenting a variety of different use cases.

Asset Management Novelty Detection

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

A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets

no code implementations13 Apr 2022 Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed

Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance.

FiN: A Smart Grid and Power Line Communication Dataset

no code implementations13 Apr 2022 Christoph Balada, Sheraz Ahmed, Andreas Dengel, Max Bondorf, Nikolai Hopfer, Markus Zdrallek

To overcome this, power line communication (PLC) has emerged as a potential solution for reliable monitoring of the low-voltage grid.

DocXClassifier: High Performance Explainable Deep Network for Document Image Classification

1 code implementation TechArXiv 2022 Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed

Our approach achieves a new peak performance in image-based classification on two popular document datasets, namely RVL-CDIP and Tobacco3482, with a top-1 classification accuracy of 94. 17% and 95. 57% on the two datasets, respectively.

Classification Data Augmentation +4

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.

Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis

no code implementations22 Feb 2022 Dominique Mercier, Syed Tahseen Raza Rizvi, Vikas Rajashekar, Sheraz Ahmed, Andreas Dengel

However, qualitative aspects provide deeper insights into the impact of a scientific research artifact and make it possible to focus on relevant literature free from bias associated with quantitative aspects.

Scheduling Sentiment Analysis

Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods

no code implementations8 Feb 2022 Dominique Mercier, Jwalin Bhatt, Andreas Dengel, Sheraz Ahmed

However, due to the lack of transparency the use of these networks is hampered in the areas with safety critical areas.

Time Series Time Series Analysis

KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting

no code implementations8 Feb 2022 Muhammad Ali Chattha, Ludger van Elst, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications.

Anomaly Detection Time Series +1

Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability

no code implementations21 Jan 2022 Alireza Koochali, Peter Schichtel, Andreas Dengel, Sheraz Ahmed

The recent developments in the machine learning domain have enabled the development of complex multivariate probabilistic forecasting models.

ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions

no code implementations4 Jan 2022 Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

This work presents ExAID (Explainable AI for Dermatology), a novel framework for biomedical image analysis, providing multi-modal concept-based explanations consisting of easy-to-understand textual explanations supplemented by visual maps justifying the predictions.

Decision Making

Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

1 code implementation29 Nov 2021 Dominique Mercier, Adriano Lucieri, Mohsin Munir, Andreas Dengel, Sheraz Ahmed

With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders.

BIG-bench Machine Learning Privacy Preserving +3

Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction

no code implementations26 May 2021 Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed

These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread.

Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis

no code implementations2 Mar 2021 Adriano Lucieri, Andreas Dengel, Sheraz Ahmed

Moreover, the possibility to intervene and guide models in case of misbehaviour is identified as a major step towards successful deployment of AI as DL-based DSS and beyond.

Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

no code implementations26 Nov 2020 Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, Sheraz Ahmed

Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable.

Decision Making

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 Benchmarking +2

Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection

no code implementations28 May 2020 Muhammad Naseer Bajwa, Yoshinobu Taniguchi, Muhammad Imran Malik, Wolfgang Neumeier, Andreas Dengel, Sheraz Ahmed

Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina.

Diabetic Retinopathy Detection

G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection

2 code implementations28 May 2020 Muhammad Naseer Bajwa, Gur Amrit Pal Singh, Wolfgang Neumeier, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD).

Optic Cup Segmentation

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

no code implementations5 May 2020 Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists.

Decision Making Image Classification +1

P2ExNet: Patch-based Prototype Explanation Network

no code implementations5 May 2020 Dominique Mercier, Andreas Dengel, Sheraz Ahmed

Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentation, and other tasks.

Time Series Time Series Analysis

ImpactCite: An XLNet-based method for Citation Impact Analysis

1 code implementation5 May 2020 Dominique Mercier, Syed Tahseen Raza Rizvi, Vikas Rajashekar, Andreas Dengel, Sheraz Ahmed

Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact.

 Ranked #1 on Citation Intent Classification on SciCite (using extra training data)

Citation Intent Classification Classification +5

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.

Explaining AI-based Decision Support Systems using Concept Localization Maps

1 code implementation4 May 2020 Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, Sheraz Ahmed

We evaluated our proposed method on SCDB as well as a real-world dataset called CelebA.

If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN

1 code implementation3 May 2020 Alireza Koochali, Andreas Dengel, Sheraz Ahmed

The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1

DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks

2 code implementations19 Apr 2020 Mateus Dias Ribeiro, Abdul Rehman, Sheraz Ahmed, Andreas Dengel

Computational Fluid Dynamics (CFD) simulation by the numerical solution of the Navier-Stokes equations is an essential tool in a wide range of applications from engineering design to climate modeling.

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 Time Series Analysis

A Robust and Precise ConvNet for small non-coding RNA classification (RPC-snRC)

no code implementations23 Dec 2019 Muhammad Nabeel Asima, Muhammad Imran Malik, Andreas Dengela, Sheraz Ahmed

In order to assess the effectiveness of deeper architectures for small non-coding RNA classification, we also adapted two ResNet architectures having different number of layers.

Classification General Classification

A Hybrid Approach and Unified Framework for Bibliographic Reference Extraction

no code implementations16 Dec 2019 Syed Tahseen Raza Rizvi, Andreas Dengel, Sheraz Ahmed

DeepBiRD was evaluated on two different datasets to demonstrate the generalization of this approach.

SentiCite: An Approach for Publication Sentiment Analysis

no code implementations7 Oct 2019 Dominique Mercier, Akansha Bhardwaj, Andreas Dengel, Sheraz Ahmed

This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e. g., reference to a dataset, reading reference.

Sentiment Analysis

A Robust Hybrid Approach for Textual Document Classification

1 code implementation12 Sep 2019 Muhammad Nabeel Asim, Muhammad Usman Ghani Khan, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7. 7% on 20 Newsgroups and 6. 6% on BBC news datasets.

BIG-bench Machine Learning Classification +5

ProbAct: A Probabilistic Activation Function for Deep Neural Networks

1 code implementation26 May 2019 Kumar Shridhar, Joonho Lee, Hideaki Hayashi, Purvanshi Mehta, Brian Kenji Iwana, Seokjun Kang, Seiichi Uchida, Sheraz Ahmed, Andreas Dengel

We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.

Image Classification

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

4 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 Time Series Anomaly Detection +1

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.

Clustering Self-Driving Cars +2

Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

5 code implementations11 Apr 2017 Muhammad Zeshan Afzal, Andreas Kölsch, Sheraz Ahmed, Marcus Liwicki

We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half.

Document Image Classification General Classification +2

TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

4 code implementations19 Mar 2017 Ayushman Dash, John Cristian Borges Gamboa, Sheraz Ahmed, Marcus Liwicki, Muhammad Zeshan Afzal

In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions.

Diversity Generative Adversarial Network +2

Judging a Book By its Cover

4 code implementations28 Oct 2016 Brian Kenji Iwana, Syed Tahseen Raza Rizvi, Sheraz Ahmed, Andreas Dengel, Seiichi Uchida

Book covers communicate information to potential readers, but can that same information be learned by computers?

Genre classification

A Novel Approach for Data-Driven Automatic Site Recommendation and Selection

no code implementations3 Aug 2016 Sebastian Baumbach, Frank Wittich, Florian Sachs, Sheraz Ahmed, Andreas Dengel

The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data.

A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents

no code implementations4 May 2016 Sheraz Ahmed, Muhammad Imran Malik, Muhammad Zeshan Afzal, Koichi Kise, Masakazu Iwamura, Andreas Dengel, Marcus Liwicki

The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e. g., English, Russian, Arabic, Urdu, etc.

Optical Character Recognition (OCR)

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