119 citations · 845 across the 65 of their papers we have counts for
70 papers
DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion
Marcel Lamott, Saifullah Saifullah, Nauman Riaz +11
Effective document intelligence models rely on large amounts of annotated training data. However, procuring sufficient and high-quality data poses significant challenges due to the…
The Sound of Death: Deep Learning Reveals Vascular Damage from Carotid Ultrasound
Christoph Balada, Aida Romano-Martinez, Payal Varshney +10
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, yet early risk detection is often limited by available diagnostics. Carotid ultrasound, a non-invasi…
LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanations
Payal Varshney, Adriano Lucieri, Christoph Balada +2
Video-based AI systems are increasingly adopted in safety-critical domains such as autonomous driving and healthcare. However, interpreting their decisions remains challenging due…
DocVCE: Diffusion-based Visual Counterfactual Explanations for Document Image Classification
Saifullah Saifullah, Stefan Agne, Andreas Dengel +1
As black-box AI-driven decision-making systems become increasingly widespread in modern document processing workflows, improving their transparency and reliability has become criti…
DP-DocLDM: Differentially Private Document Image Generation using Latent Diffusion Models
Saifullah Saifullah, Stefan Agne, Andreas Dengel +1
As deep learning-based, data-driven information extraction systems become increasingly integrated into modern document processing workflows, one primary concern is the risk of mali…
FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep neural networks are among the most successful algorithms in terms of performance and scalability across different domains. However, since these networks are black boxes, their…
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering
Payal Varshney, Adriano Lucieri, Christoph Balada +2
Concept-based explanations have emerged as an effective approach within Explainable Artificial Intelligence, enabling interpretable insights by aligning model decisions with human-…
Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage
Christoph Balada, Aida Romano-Martinez, Vincent ten Cate +9
In this study, hypertension is utilized as an indicator of individual vascular damage. This damage can be identified through machine learning techniques, providing an early risk ma…
WordVIS: A Color Worth A Thousand Words
Umar Khan, Saifullah, Stefan Agne +2
Document classification is considered a critical element in automated document processing systems. In recent years multi-modal approaches have become increasingly popular for docum…
Latent Diffusion for Guided Document Table Generation
Syed Jawwad Haider Hamdani, Saifullah Saifullah, Stefan Agne +2
Obtaining annotated table structure data for complex tables is a challenging task due to the inherent diversity and complexity of real-world document layouts. The scarcity of publi…
StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation
Nauman Riaz, Saifullah Saifullah, Stefan Agne +2
In this study, we introduce StylusAI, a novel architecture leveraging diffusion models in the domain of handwriting style generation. StylusAI is specifically designed to adapt and…
DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification
Saifullah Saifullah, Stefan Agne, Andreas Dengel +1
Deep learning (DL) has revolutionized the field of document image analysis, showcasing superhuman performance across a diverse set of tasks. However, the inherent black-box nature…
VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting
Alireza Koochali, Ensiye Tahaei, Andreas Dengel +1
This paper presents VAEneu, an innovative autoregressive method for multistep ahead univariate probabilistic time series forecasting. We employ the conditional VAE framework and op…
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning
Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed
Detecting diseases from social media has diverse applications, such as public health monitoring and disease spread detection. While language models (LMs) have shown promising perfo…
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
Payal Varshney, Adriano Lucieri, Christoph Balada +2
Trustworthiness is a major prerequisite for the safe application of opaque deep learning models in high-stakes domains like medicine. Understanding the decision-making process not…
Class Conditional Time Series Generation with Structured Noise Space GAN
Hamidreza Gholamrezaei, Alireza Koochali, Andreas Dengel +1
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 imag…
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification
Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed
There are not many large medical image datasets available. For these datasets, too small deep learning models can't learn useful features, so they don't work well due to underfitti…
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content
Pervaiz Iqbal Khan, Muhammad Nabeel Asim, Andreas Dengel +1
An ever-increasing amount of social media content requires advanced AI-based computer programs capable of extracting useful information. Specifically, the extraction of health-rela…
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction
Saifullah Saifullah, Stefan Agne, Andreas Dengel +1
In this paper, we introduce strategies for developing private Key Information Extraction (KIE) systems by leveraging large pretrained document foundation models in conjunction with…
From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep learning has proven to be successful in various domains and for different tasks. However, when it comes to private data several restrictions are making it difficult to use dee…
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction
Amin E. Bakhshipour, Alireza Koochali, Ulrich Dittmer +3
Despite various breakthroughs in machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obs…
Privacy Meets Explainability: A Comprehensive Impact Benchmark
Saifullah Saifullah, Dominique Mercier, Adriano Lucieri +2
Since the mid-10s, the era of Deep Learning (DL) has continued to this day, bringing forth new superlatives and innovations each year. Nevertheless, the speed with which these inno…
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain
Alireza Koochali, Maria Walch, Sankrutyayan Thota +3
Generative models are designed to address the data scarcity problem. Even with the exploding amount of data, due to computational advancements, some applications (e.g., health care…
Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification
Adriano Lucieri, Fabian Schmeisser, Christoph Peter Balada +3
It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to…