Search Results for author: Alexander Binder

Found 37 papers, 12 papers with code

Layer-wise Feedback Propagation

no code implementations23 Aug 2023 Leander Weber, Jim Berend, Alexander Binder, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

In this paper, we present Layer-wise Feedback Propagation (LFP), a novel training approach for neural-network-like predictors that utilizes explainability, specifically Layer-wise Relevance Propagation(LRP), to assign rewards to individual connections based on their respective contributions to solving a given task.

Transfer Learning

Optimizing Explanations by Network Canonization and Hyperparameter Search

no code implementations30 Nov 2022 Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin

We further suggest a XAI evaluation framework with which we quantify and compare the effect sof model canonization for various XAI methods in image classification tasks on the Pascal-VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI.

Explainable Artificial Intelligence (XAI) Image Classification +2

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

no code implementations CVPR 2023 Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

To address shortcomings of this test, we start by observing an experimental gap in the ranking of explanation methods between randomization-based sanity checks [1] and model output faithfulness measures (e. g. [25]).

Discovering Transferable Forensic Features for CNN-generated Images Detection

1 code implementation24 Aug 2022 Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Alexander Binder, Ngai-Man Cheung

Visual counterfeits are increasingly causing an existential conundrum in mainstream media with rapid evolution in neural image synthesis methods.

Image Forensics Image Generation

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

no code implementations15 Mar 2022 Leander Weber, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek

We conclude that while model improvement based on XAI can have significant beneficial effects even on complex and not easily quantifyable model properties, these methods need to be applied carefully, since their success can vary depending on a multitude of factors, such as the model and dataset used, or the employed explanation method.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples

no code implementations24 Oct 2021 Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun, Ngai-Man Cheung, Yuval Elovici, Alexander Binder

In this work, we aim to close this gap by studying a conceptually simple approach to defend few-shot classifiers against adversarial attacks.

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

no code implementations25 Jun 2021 Vignesh Srinivasan, Nils Strodthoff, Jackie Ma, Alexander Binder, Klaus-Robert Müller, Wojciech Samek

Our results indicate that models initialized from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.

Contrastive Learning Diabetic Retinopathy Grading

Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering

no code implementations9 Dec 2020 Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun, Ngai-Man Cheung, Yuval Elovici, Alexander Binder

In this work, we propose a detection strategy to identify adversarial support sets, aimed at destroying the understanding of a few-shot classifier for a certain class.

Toward Scalable and Unified Example-based Explanation and Outlier Detection

no code implementations11 Nov 2020 Penny Chong, Ngai-Man Cheung, Yuval Elovici, Alexander Binder

We compare performances in terms of the classification, explanation quality, and outlier detection of our proposed network with other baselines.

Decision Making Outlier Detection

Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation

no code implementations21 Jul 2020 Lin Geng Foo, Rui En Ho, Jiamei Sun, Alexander Binder

In this work, we propose a two-step post-processing procedure, Split and Expand, that directly improves the conversion of segmentation maps to instances.

Bias Detection Instance Segmentation +2

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

1 code implementation17 Jul 2020 Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, Alexander Binder

It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models.

Classification Cross-Domain Few-Shot +1

Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution

1 code implementation arXiv 2020 Gary S. W. Goh, Sebastian Lapuschkin, Leander Weber, Wojciech Samek, Alexander Binder

From our experiments, we find that the SmoothTaylor approach together with adaptive noising is able to generate better quality saliency maps with lesser noise and higher sensitivity to the relevant points in the input space as compared to Integrated Gradients.

Image Classification Object Recognition

Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification

no code implementations24 Jan 2020 Penny Chong, Lukas Ruff, Marius Kloft, Alexander Binder

However, deep SVDD suffers from hypersphere collapse -- also known as mode collapse, if the architecture of the model does not comply with certain architectural constraints, e. g. the removal of bias terms.

General Classification One-Class Classification

Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models

1 code implementation4 Jan 2020 Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Alexander Binder

We develop variants of layer-wise relevance propagation (LRP) and gradient-based explanation methods, tailored to image captioning models with attention mechanisms.

Hallucination Image Captioning +2

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

1 code implementation18 Dec 2019 Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs.

Explainable Artificial Intelligence (XAI) Model Compression +2

Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

no code implementations28 May 2019 Yi Xiang Marcus Tan, Alfonso Iacovazzi, Ivan Homoliak, Yuval Elovici, Alexander Binder

In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models.

BIG-bench Machine Learning

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

1 code implementation26 Feb 2019 Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior.

SmartOTPs: An Air-Gapped 2-Factor Authentication for Smart-Contract Wallets (Extended Version)

2 code implementations10 Dec 2018 Ivan Homoliak, Dominik Breitenbacher, Ondrej Hujnak, Pieter Hartel, Alexander Binder, Pawel Szalachowski

The proposed framework consists of four components (i. e., an authenticator, a client, a hardware wallet, and a smart contract), and it provides 2-factor authentication (2FA) performed in two stages of interaction with the blockchain.

Cryptography and Security

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.

Classification One-Class Classification +1

Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation

no code implementations24 Nov 2016 Wojciech Samek, Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Klaus-Robert Müller

Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application.

General Classification Image Classification +2

Object Boundary Detection and Classification with Image-level Labels

no code implementations29 Jun 2016 Jing Yu Koh, Wojciech Samek, Klaus-Robert Müller, Alexander Binder

We propose a novel strategy for solving this task, when pixel-level annotations are not available, performing it in an almost zero-shot manner by relying on conventional whole image neural net classifiers that were trained using large bounding boxes.

Boundary Detection Classification +4

Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

no code implementations4 Apr 2016 Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller, Wojciech Samek

Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e. g. an image, down to relevance scores for the single input dimensions of the sample such as subpixels of an image.

Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth

no code implementations21 Mar 2016 Sebastian Bach, Alexander Binder, Klaus-Robert Müller, Wojciech Samek

We present an application of the Layer-wise Relevance Propagation (LRP) algorithm to state of the art deep convolutional neural networks and Fisher Vector classifiers to compare the image perception and prediction strategies of both classifiers with the use of visualized heatmaps.


Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

4 code implementations8 Dec 2015 Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, Klaus-Robert Müller

Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures.

Action Recognition Classification +3

Evaluating the visualization of what a Deep Neural Network has learned

1 code implementation21 Sep 2015 Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller

Our main result is that the recently proposed Layer-wise Relevance Propagation (LRP) algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method.

Classification General Classification +4

Localized Multiple Kernel Learning---A Convex Approach

no code implementations14 Jun 2015 Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft

We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure.

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms

no code implementations NeurIPS 2015 Yunwen Lei, Ürün Dogan, Alexander Binder, Marius Kloft

This paper studies the generalization performance of multi-class classification algorithms, for which we obtain, for the first time, a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent generalization analysis.

General Classification Generalization Bounds +1

Multiple Kernel Learning for Brain-Computer Interfacing

no code implementations22 Oct 2013 Wojciech Samek, Alexander Binder, Klaus-Robert Müller

Combining information from different sources is a common way to improve classification accuracy in Brain-Computer Interfacing (BCI).

General Classification

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