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Understanding Neural Networks Through Deep Visualization

Understanding Neural Networks Through Deep Visualization

22 June 2015
J. Yosinski
Jeff Clune
Anh Totti Nguyen
Thomas J. Fuchs
Hod Lipson
    FAtt
    AI4CE
ArXivPDFHTML

Papers citing "Understanding Neural Networks Through Deep Visualization"

50 / 298 papers shown
Title
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for
  Efficient Deep-Reinforcement Learning
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning
Adarsh Kosta
Malik Aqeel Anwar
Priyadarshini Panda
A. Raychowdhury
Kaushik Roy
13
4
0
16 Sep 2021
IFBiD: Inference-Free Bias Detection
IFBiD: Inference-Free Bias Detection
Ignacio Serna
Daniel DeAlcala
Aythami Morales
Julian Fierrez
J. Ortega-Garcia
CVBM
39
11
0
09 Sep 2021
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in
  Deep Neural Networks
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Haekyu Park
Nilaksh Das
Rahul Duggal
Austin P. Wright
Omar Shaikh
Fred Hohman
Duen Horng Chau
HAI
19
25
0
29 Aug 2021
This looks more like that: Enhancing Self-Explaining Models by
  Prototypical Relevance Propagation
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Srishti Gautam
Marina M.-C. Höhne
Stine Hansen
Robert Jenssen
Michael C. Kampffmeyer
27
49
0
27 Aug 2021
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual
  Features in Images
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images
Jiafan Zhuang
Wanying Tao
Jianfei Xing
Wei Shi
Ruixuan Wang
Weishi Zheng
FAtt
42
3
0
25 Aug 2021
Explaining Bayesian Neural Networks
Explaining Bayesian Neural Networks
Kirill Bykov
Marina M.-C. Höhne
Adelaida Creosteanu
Klaus-Robert Muller
Frederick Klauschen
Shinichi Nakajima
Marius Kloft
BDL
AAML
34
25
0
23 Aug 2021
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Upol Ehsan
Samir Passi
Q. V. Liao
Larry Chan
I-Hsiang Lee
Michael J. Muller
Mark O. Riedl
32
86
0
28 Jul 2021
Rethinking Hard-Parameter Sharing in Multi-Domain Learning
Rethinking Hard-Parameter Sharing in Multi-Domain Learning
Lijun Zhang
Qizheng Yang
Xiao Liu
Hui Guan
OOD
31
14
0
23 Jul 2021
On The Distribution of Penultimate Activations of Classification
  Networks
On The Distribution of Penultimate Activations of Classification Networks
Minkyo Seo
Yoonho Lee
Suha Kwak
UQCV
18
4
0
05 Jul 2021
Normalizing Flow based Hidden Markov Models for Classification of Speech
  Phones with Explainability
Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability
Anubhab Ghosh
Antoine Honoré
Dong Liu
G. Henter
S. Chatterjee
16
5
0
01 Jul 2021
Inverting and Understanding Object Detectors
Inverting and Understanding Object Detectors
Ang Cao
Justin Johnson
ObjD
33
3
0
26 Jun 2021
Attack to Fool and Explain Deep Networks
Attack to Fool and Explain Deep Networks
Naveed Akhtar
M. Jalwana
Bennamoun
Ajmal Mian
AAML
27
33
0
20 Jun 2021
Explainable Machine Learning with Prior Knowledge: An Overview
Explainable Machine Learning with Prior Knowledge: An Overview
Katharina Beckh
Sebastian Müller
Matthias Jakobs
Vanessa Toborek
Hanxiao Tan
Raphael Fischer
Pascal Welke
Sebastian Houben
Laura von Rueden
XAI
22
28
0
21 May 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Eric Wong
Shibani Santurkar
A. Madry
FAtt
22
88
0
11 May 2021
Carrying out CNN Channel Pruning in a White Box
Carrying out CNN Channel Pruning in a White Box
Yuxin Zhang
Mingbao Lin
Chia-Wen Lin
Jie Chen
Feiyue Huang
Yongjian Wu
Yonghong Tian
Rongrong Ji
VLM
39
58
0
24 Apr 2021
Deep Recursive Embedding for High-Dimensional Data
Zixia Zhou
Yuanyuan Wang
B. Lelieveldt
Qian Tao
24
7
0
12 Apr 2021
Explainable Adversarial Attacks in Deep Neural Networks Using Activation
  Profiles
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles
G. Cantareira
R. Mello
F. Paulovich
AAML
24
9
0
18 Mar 2021
Intraclass clustering: an implicit learning ability that regularizes
  DNNs
Intraclass clustering: an implicit learning ability that regularizes DNNs
Simon Carbonnelle
Christophe De Vleeschouwer
60
8
0
11 Mar 2021
How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D
  Lines
How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines
Kunal Chelani
Fredrik Kahl
Torsten Sattler
3DPC
32
23
0
08 Mar 2021
BENDR: using transformers and a contrastive self-supervised learning
  task to learn from massive amounts of EEG data
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Demetres Kostas
Stephane Aroca-Ouellette
Frank Rudzicz
SSL
46
203
0
28 Jan 2021
Deep Neural Models for color discrimination and color constancy
Deep Neural Models for color discrimination and color constancy
Alban Flachot
A. Akbarinia
Heiko H. Schutt
R. Fleming
Felix Wichmann
K. Gegenfurtner
19
2
0
28 Dec 2020
Understanding Failures of Deep Networks via Robust Feature Extraction
Understanding Failures of Deep Networks via Robust Feature Extraction
Sahil Singla
Besmira Nushi
S. Shah
Ece Kamar
Eric Horvitz
FAtt
28
83
0
03 Dec 2020
Dank or Not? -- Analyzing and Predicting the Popularity of Memes on
  Reddit
Dank or Not? -- Analyzing and Predicting the Popularity of Memes on Reddit
Kate Barnes
Tiernon R. Riesenmy
Minh Duc Trinh
Eli Lleshi
Nóra Balogh
Roland Molontay
19
31
0
29 Nov 2020
Quantifying Explainers of Graph Neural Networks in Computational
  Pathology
Quantifying Explainers of Graph Neural Networks in Computational Pathology
Guillaume Jaume
Pushpak Pati
Behzad Bozorgtabar
Antonio Foncubierta-Rodríguez
Florinda Feroce
A. Anniciello
T. Rau
Jean-Philippe Thiran
M. Gabrani
O. Goksel
FAtt
26
76
0
25 Nov 2020
Dirichlet Pruning for Neural Network Compression
Dirichlet Pruning for Neural Network Compression
Kamil Adamczewski
Mijung Park
27
3
0
10 Nov 2020
Exemplary Natural Images Explain CNN Activations Better than
  State-of-the-Art Feature Visualization
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
Judy Borowski
Roland S. Zimmermann
Judith Schepers
Robert Geirhos
Thomas S. A. Wallis
Matthias Bethge
Wieland Brendel
FAtt
42
7
0
23 Oct 2020
Interpreting convolutional networks trained on textual data
Interpreting convolutional networks trained on textual data
Reza Marzban
Christopher Crick
FAtt
27
3
0
20 Oct 2020
Knowledge-Enriched Distributional Model Inversion Attacks
Knowledge-Enriched Distributional Model Inversion Attacks
Si-An Chen
Mostafa Kahla
R. Jia
Guo-Jun Qi
24
93
0
08 Oct 2020
Spatial Attention as an Interface for Image Captioning Models
Spatial Attention as an Interface for Image Captioning Models
P. Sadler
28
0
0
29 Sep 2020
The FaceChannel: A Fast & Furious Deep Neural Network for Facial
  Expression Recognition
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition
Pablo V. A. Barros
Nikhil Churamani
A. Sciutti
CVBM
21
38
0
15 Sep 2020
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray
  Scattering Images
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Xinyi Huang
Suphanut Jamonnak
Ye Zhao
Boyu Wang
Minh Hoai
Kevin Yager
Wei-ping Xu
30
5
0
03 Sep 2020
Perceptual underwater image enhancement with deep learning and physical
  priors
Perceptual underwater image enhancement with deep learning and physical priors
Long Chen
Zheheng Jiang
Lei Tong
Zhihua Liu
Aite Zhao
Qianni Zhang
Junyu Dong
Huiyu Zhou
24
33
0
21 Aug 2020
Survey of XAI in digital pathology
Survey of XAI in digital pathology
Milda Pocevičiūtė
Gabriel Eilertsen
Claes Lundström
14
56
0
14 Aug 2020
Explainable Predictive Process Monitoring
Explainable Predictive Process Monitoring
Musabir Musabayli
F. Maggi
Williams Rizzi
Josep Carmona
Chiara Di Francescomarino
14
60
0
04 Aug 2020
The Representation Theory of Neural Networks
The Representation Theory of Neural Networks
M. Armenta
Pierre-Marc Jodoin
29
30
0
23 Jul 2020
End-to-end Learning of Compressible Features
End-to-end Learning of Compressible Features
Saurabh Singh
Sami Abu-El-Haija
Nick Johnston
Johannes Ballé
Abhinav Shrivastava
G. Toderici
SSL
97
71
0
23 Jul 2020
Adversarial Training Reduces Information and Improves Transferability
Adversarial Training Reduces Information and Improves Transferability
M. Terzi
Alessandro Achille
Marco Maggipinto
Gian Antonio Susto
AAML
24
23
0
22 Jul 2020
A Survey of Privacy Attacks in Machine Learning
A Survey of Privacy Attacks in Machine Learning
M. Rigaki
Sebastian Garcia
PILM
AAML
39
213
0
15 Jul 2020
Decoding CNN based Object Classifier Using Visualization
Decoding CNN based Object Classifier Using Visualization
Abhishek Mukhopadhyay
Imon Mukherjee
P. Biswas
24
3
0
15 Jul 2020
Interpreting and Disentangling Feature Components of Various Complexity
  from DNNs
Interpreting and Disentangling Feature Components of Various Complexity from DNNs
Jie Ren
Mingjie Li
Zexu Liu
Quanshi Zhang
CoGe
19
18
0
29 Jun 2020
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data
  Distillation and Augmentation
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation
D. Lu
N. Polomac
Iskra Gacheva
E. Hattingen
Jochen Triesch
18
18
0
17 Jun 2020
A generalizable saliency map-based interpretation of model outcome
A generalizable saliency map-based interpretation of model outcome
Shailja Thakur
S. Fischmeister
AAML
FAtt
MILM
24
2
0
16 Jun 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining
  Neural Networks
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks
Kirill Bykov
Marina M.-C. Höhne
Klaus-Robert Muller
Shinichi Nakajima
Marius Kloft
UQCV
FAtt
27
31
0
16 Jun 2020
AI Research Considerations for Human Existential Safety (ARCHES)
AI Research Considerations for Human Existential Safety (ARCHES)
Andrew Critch
David M. Krueger
30
50
0
30 May 2020
CNN Explainer: Learning Convolutional Neural Networks with Interactive
  Visualization
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Zijie J. Wang
Robert Turko
Omar Shaikh
Haekyu Park
Nilaksh Das
Fred Hohman
Minsuk Kahng
Duen Horng Chau
HAI
FAtt
35
240
0
30 Apr 2020
Explainable Deep Learning: A Field Guide for the Uninitiated
Explainable Deep Learning: A Field Guide for the Uninitiated
Gabrielle Ras
Ning Xie
Marcel van Gerven
Derek Doran
AAML
XAI
41
371
0
30 Apr 2020
InsideBias: Measuring Bias in Deep Networks and Application to Face
  Gender Biometrics
InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics
Ignacio Serna
Alejandro Peña
Aythami Morales
Julian Fierrez
CVBM
14
61
0
14 Apr 2020
X3D: Expanding Architectures for Efficient Video Recognition
X3D: Expanding Architectures for Efficient Video Recognition
Christoph Feichtenhofer
73
1,001
0
09 Apr 2020
TSInsight: A local-global attribution framework for interpretability in
  time-series data
TSInsight: A local-global attribution framework for interpretability in time-series data
Shoaib Ahmed Siddiqui
Dominique Mercier
Andreas Dengel
Sheraz Ahmed
FAtt
AI4TS
13
12
0
06 Apr 2020
A Survey of Deep Learning for Scientific Discovery
A Survey of Deep Learning for Scientific Discovery
M. Raghu
Erica Schmidt
OOD
AI4CE
40
120
0
26 Mar 2020
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