Wojciech Samek

research

∙ 08/23/2023

Layer-wise Feedback Propagation

In this paper, we present Layer-wise Feedback Propagation (LFP), a novel...

1 Leander Weber, et al. ∙

research

∙ 08/18/2023

From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space

Deep Neural Networks are prone to learning spurious correlations embedde...

0 Maximilian Dreyer, et al. ∙

research

∙ 06/23/2023

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology

We present DiffInfinite, a hierarchical diffusion model that generates a...

0 Marco Aversa, et al. ∙

research

∙ 04/27/2023

XAI-based Comparison of Input Representations for Audio Event Classification

Deep neural networks are a promising tool for Audio Event Classification...

0 Annika Frommholz, et al. ∙

research

∙ 03/22/2023

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models

State-of-the-art machine learning models often learn spurious correlatio...

0 Frederik Pahde, et al. ∙

research

∙ 03/11/2023

Explainable AI for Time Series via Virtual Inspection Layers

The field of eXplainable Artificial Intelligence (XAI) has greatly advan...

0 Johanna Vielhaben, et al. ∙

research

∙ 03/07/2023

A Privacy Preserving System for Movie Recommendations using Federated Learning

Recommender systems have become ubiquitous in the past years. They solve...

0 David Neumann, et al. ∙

research

∙ 02/14/2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus

Explainable AI (XAI) is a rapidly evolving field that aims to improve tr...

0 Anna Hedström, et al. ∙

research

∙ 11/30/2022

Optimizing Explanations by Network Canonization and Hyperparameter Search

Explainable AI (XAI) is slowly becoming a key component for many AI appl...

0 Frederik Pahde, et al. ∙

research

∙ 11/22/2022

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

While the evaluation of explanations is an important step towards trustw...

0 Alexander Binder, et al. ∙

research

∙ 11/21/2022

Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations

Applying traditional post-hoc attribution methods to segmentation or obj...

0 Maximilian Dreyer, et al. ∙

research

∙ 11/04/2022

Data Models for Dataset Drift Controls in Machine Learning With Images

Camera images are ubiquitous in machine learning research. They also pla...

1 Luis Oala, et al. ∙

research

∙ 06/07/2022

From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation

The emerging field of eXplainable Artificial Intelligence (XAI) aims to ...

0 Reduan Achtibat, et al. ∙

research

∙ 05/30/2022

FedAUXfdp: Differentially Private One-Shot Federated Distillation

Federated learning suffers in the case of non-iid local datasets, i.e., ...

0 Haley Hoech, et al. ∙

research

∙ 05/07/2022

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

The advent of Federated Learning (FL) has ignited a new paradigm for par...

0 Leon Witt, et al. ∙

research

∙ 05/04/2022

Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI

The ability to continuously process and retain new information like we d...

0 Sami Ede, et al. ∙

research

∙ 04/09/2022

Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning

Federated learning (FL) scenarios inherently generate a large communicat...

0 Daniel Becking, et al. ∙

research

∙ 03/15/2022

Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement

Explainable Artificial Intelligence (XAI) is an emerging research field ...

0 Leander Weber, et al. ∙

research

∙ 02/14/2022

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations

The evaluation of explanation methods is a research topic that has not y...

0 Anna Hedström, et al. ∙

research

∙ 02/07/2022

PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging

State-of-the-art machine learning models are commonly (pre-)trained on l...

0 Frederik Pahde, et al. ∙

research

∙ 12/21/2021

Toward Explainable AI for Regression Models

In addition to the impressive predictive power of machine learning (ML) ...

83 Simon Letzgus, et al. ∙

research

∙ 11/01/2021

Evaluating deep transfer learning for whole-brain cognitive decoding

Research in many fields has shown that transfer learning (TL) is well-su...

13 Armin W. Thomas, et al. ∙

research

∙ 09/09/2021

ECQ^x: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

The remarkable success of deep neural networks (DNNs) in various applica...

0 Daniel Becking, et al. ∙

research

∙ 06/27/2021

Reward-Based 1-bit Compressed Federated Distillation on Blockchain

The recent advent of various forms of Federated Knowledge Distillation (...

0 Leon Witt, et al. ∙

research

∙ 06/25/2021

On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy

There is an increasing number of medical use-cases where classification ...

10 Vignesh Srinivasan, et al. ∙

research

∙ 06/24/2021

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

Deep Neural Networks (DNNs) are known to be strong predictors, but their...

20 Christopher J. Anders, et al. ∙

research

∙ 02/04/2021

FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning

Federated Distillation (FD) is a popular novel algorithmic paradigm for ...

13 Felix Sattler, et al. ∙

research

∙ 12/17/2020

FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons

With the growing demand for deploying deep learning models to the "edge"...

0 Simon Wiedemann, et al. ∙

research

∙ 12/01/2020

Communication-Efficient Federated Distillation

Communication constraints are one of the major challenges preventing the...

0 Felix Sattler, et al. ∙

research

∙ 09/24/2020

A Unifying Review of Deep and Shallow Anomaly Detection

Deep learning approaches to anomaly detection have recently improved the...

49 Lukas Ruff, et al. ∙

research

∙ 08/31/2020

Langevin Cooling for Domain Translation

Domain translation is the task of finding correspondence between two dom...

5 Vignesh Srinivasan, et al. ∙

research

∙ 07/17/2020

Explanation-Guided Training for Cross-Domain Few-Shot Classification

Cross-domain few-shot classification task (CD-FSC) combines few-shot cla...

22 Jiamei Sun, et al. ∙

research

∙ 06/14/2020

MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures

In this work, we propose MixMOOD - a systematic approach to mitigate eff...

1 Saul Calderon-Ramirez, et al. ∙

research

∙ 06/09/2020

Sensor Artificial Intelligence and its Application to Space Systems – A White Paper

Information and communication technologies have accompanied our everyday...

0 Anko Börner, et al. ∙

research

∙ 04/28/2020

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Electrocardiography is a very common, non-invasive diagnostic procedure ...

16 Nils Strodthoff, et al. ∙

research

∙ 04/22/2020

Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements

Digital contact tracing approaches based on Bluetooth low energy (BLE) h...

13 Felix Sattler, et al. ∙

research

∙ 04/22/2020

Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution

Integrated gradients as an attribution method for deep neural network mo...

5 Gary S. W. Goh, et al. ∙

research

∙ 04/09/2020

Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training

Deep Neural Networks are successful but highly computationally expensive...

0 Simon Wiedemann, et al. ∙

research

∙ 04/02/2020

Learning Sparse Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)

Deep neural networks (DNN) have shown remarkable success in a variety of...

0 Arturo Marban, et al. ∙

research

∙ 03/27/2020

Interval Neural Networks as Instability Detectors for Image Reconstructions

This work investigates the detection of instabilities that may occur whe...

1 Jan Macdonald, et al. ∙

research

∙ 03/25/2020

Interval Neural Networks: Uncertainty Scores

We propose a fast, non-Bayesian method for producing uncertainty scores ...

9 Luis Oala, et al. ∙

research

∙ 03/17/2020

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond

With the broader and highly successful usage of machine learning in indu...

7 Wojciech Samek, et al. ∙

research

∙ 03/16/2020

Towards Ground Truth Evaluation of Visual Explanations

Several methods have been proposed to explain the decisions of neural ne...

4 Ahmed Osman, et al. ∙

research

∙ 03/06/2020

Trends and Advancements in Deep Neural Network Communication

Due to their great performance and scalability properties neural network...

0 Felix Sattler, et al. ∙

research

∙ 01/04/2020

Understanding Image Captioning Models beyond Visualizing Attention

This paper explains predictions of image captioning models with attentio...

21 Jiamei Sun, et al. ∙

research

∙ 12/22/2019

Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed

Today's machine learning models for computer vision are typically traine...

41 Christopher J. Anders, et al. ∙

research

∙ 12/18/2019

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning

The success of convolutional neural networks (CNNs) in various applicati...

68 Seul-Ki Yeom, et al. ∙

research

∙ 12/16/2019

On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence

Systems incorporating Artificial Intelligence (AI) and machine learning ...

18 Fabian Horst, et al. ∙

research

∙ 10/29/2019

Asymptotically Unbiased Generative Neural Sampling

We propose a general framework for the estimation of observables with ge...

0 Kim A. Nicoli, et al. ∙

research

∙ 10/22/2019

Towards best practice in explaining neural network decisions with LRP

Within the last decade, neural network based predictors have demonstrate...

0 Maximilian Kohlbrenner, et al. ∙

Wojciech Samek

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