3 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach
Lars Nieradzik, Henrike Stephani, Janis Keuper
In this paper, we present an approach for evaluating attribution maps, which play a central role in interpreting the predictions of convolutional neural networks (CNNs). We show th…
WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images
Lars Nieradzik, Henrike Stephani, Jördis Sieburg-Rockel +4
Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper intro…
Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation
Lars Nieradzik, Henrike Stephani, Janis Keuper
This paper introduces Top-GAP, a novel regularization technique that enhances the explainability and robustness of convolutional neural networks. By constraining the spatial size o…
Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestry
Lars Nieradzik, Henrike Stephani, Jördis Sieburg-Rockel +3
In this study, we explore the explainability of neural networks in agriculture and forestry, specifically in fertilizer treatment classification and wood identification. The opaque…
Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
Lars Nieradzik, Jördis Sieburg-Rockel, Stephanie Helmling +4
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera.…