Network Dissection: Quantifying Interpretability of Deep Visual Representations

Type: Preprint

Publication Date: 2017-04-19

Citations: 147

Locations

  • arXiv (Cornell University) - View

Similar Works

Action Title Year Authors
+ Network Dissection: Quantifying Interpretability of Deep Visual Representations 2017 David Bau
Bolei Zhou
Aditya Khosla
Aude Oliva
Antonio Torralba
+ PDF Chat Network Dissection: Quantifying Interpretability of Deep Visual Representations 2017 David Bau
Bolei Zhou
Aditya Khosla
Aude Oliva
Antonio Torralba
+ Interpreting Deep Visual Representations via Network Dissection 2017 Bolei Zhou
David Bau
Aude Oliva
Antonio Torralba
+ Visual Interpretability for Deep Learning: a Survey 2018 Quanshi Zhang
Song‐Chun Zhu
+ PDF Chat Linking in Style: Understanding learned features in deep learning models 2024 Maren H. Wehrheim
Pamela Osuna-Vargas
Matthias Kaschube
+ PDF Chat Visual interpretability for deep learning: a survey 2018 Quanshi Zhang
Song‐Chun Zhu
+ PDF Chat Understanding the role of individual units in a deep neural network 2020 David Bau
Jun-Yan Zhu
Hendrik Strobelt
Àgata Lapedriza
Bolei Zhou
Antonio Torralba
+ Labeling Neural Representations with Inverse Recognition 2023 Kirill Bykov
Laura Kopf
Shinichi Nakajima
Marius Kloft
Marina M. -C. Höhne
+ Visualizing and Comparing Convolutional Neural Networks 2014 Wei Yu
Kuiyuan Yang
Yalong Bai
Hongxun Yao
Yong Rui
+ DISCOVER: Making Vision Networks Interpretable via Competition and Dissection 2023 Konstantinos P. Panousis
Sotirios Chatzis
+ Visualizing and Understanding Convolutional Networks 2013 Matthew D. Zeiler
Rob Fergus
+ Visualizing and Understanding Convolutional Networks 2013 Matthew D. Zeiler
Rob Fergus
+ Learning Interpretable Concept Groups in CNNs 2021 Saurabh Varshneya
Antoine Ledent
Robert A. Vandermeulen
Yunwen Lei
Matthias Enders
Damian Borth
Marius Kloft
+ PDF Chat A separability-based approach to quantifying generalization: which layer is best? 2024 Luciano Dyballa
Evan Gerritz
Steven W. Zucker
+ FICNN: A Framework for the Interpretation of Deep Convolutional Neural Networks 2023 Hamed Behzadi-Khormouji
José Oramas
+ The Mind's Eye: Visualizing Class-Agnostic Features of CNNs 2021 Alexandros Stergiou
+ The Mind's Eye: Visualizing Class-Agnostic Features of CNNs 2021 Alexandros Stergiou
+ GAN Dissection: Visualizing and Understanding Generative Adversarial Networks 2018 David Bau
Jun-Yan Zhu
Hendrik Strobelt
Bolei Zhou
Joshua B. Tenenbaum
William T. Freeman
Antonio Torralba
+ Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs 2020 Robin Rombach
Patrick Esser
Björn Ommer
+ Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs 2020 Robin Rombach
Patrick Esser
Björn Ommer