Take 5: Interpretable Image Classification with a Handful of Features

Take 5: Interpretable Image Classification with a Handful of Features

23 March 2023

Thomas Norrenbrock

Papers citing "Take 5: Interpretable Image Classification with a Handful of Features"

10 / 10 papers shown

Title
QPM: Discrete Optimization for Globally Interpretable Image Classification Thomas Norrenbrock T. Kaiser Sovan Biswas R. Manuvinakurike Bodo Rosenhahn 55 0 0 27 Feb 2025
ViTree: Single-path Neural Tree for Step-wise Interpretable Fine-grained Visual Categorization Danning Lao Qi Liu Jiazi Bu Junchi Yan Wei Shen 16 1 0 30 Jan 2024
Q-SENN: Quantized Self-Explaining Neural Networks Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt AAML MILM 14 6 0 21 Dec 2023
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization Patrick Glandorf Timo Kaiser Bodo Rosenhahn 31 5 0 14 Aug 2023
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Y. Zou 133 183 0 31 May 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 58 114 0 06 Dec 2021
Interpretable Image Classification with Differentiable Prototypes Assignment Dawid Rymarczyk Lukasz Struski Michal Górszczak K. Lewandowska Jacek Tabor Bartosz Zieliñski 31 98 0 06 Dec 2021
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection Marco Rudolph Tom Wehrbein Bodo Rosenhahn Bastian Wandt UQCV 73 203 0 06 Oct 2021
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 247 36,237 0 25 Aug 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,170 0 01 Sep 2014