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Striving for Simplicity: The All Convolutional Net
v1v2v3 (latest)

Striving for Simplicity: The All Convolutional Net

International Conference on Learning Representations (ICLR), 2014
21 December 2014
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
    FAtt
ArXiv (abs)PDFHTML

Papers citing "Striving for Simplicity: The All Convolutional Net"

50 / 1,916 papers shown
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
Anders Gjølbye
Stefan Haufe
Lars Kai Hansen
619
1
0
16 May 2025
Feature Visualization in 3D Convolutional Neural Networks
Feature Visualization in 3D Convolutional Neural NetworksInternational Conference on Intelligent Computing (ICIC), 2025
Chunpeng Li
Ya-tang Li
FAtt
199
0
0
12 May 2025
Explainable AI the Latest Advancements and New Trends
Explainable AI the Latest Advancements and New Trends
Bowen Long
Enjie Liu
Renxi Qiu
Yanqing Duan
XAI
739
2
0
11 May 2025
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
Moritz Vandenhirtz
Julia E. Vogt
401
1
0
09 May 2025
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
Kien Tran Duc Tuan
Tam Nguyen Trong
Son Nguyen Hoang
Khoat Than
Anh Nguyen Duc
FAtt
246
0
0
06 May 2025
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Sonal Allana
Mohan Kankanhalli
Rozita Dara
370
3
0
05 May 2025
Explainable Face Recognition via Improved Localization
Explainable Face Recognition via Improved Localization
Rashik Shadman
Daqing Hou
Faraz Hussain
M. G. Sarwar Murshed
CVBMFAtt
184
1
0
04 May 2025
What Makes for a Good Saliency Map? Comparing Strategies for Evaluating Saliency Maps in Explainable AI (XAI)
What Makes for a Good Saliency Map? Comparing Strategies for Evaluating Saliency Maps in Explainable AI (XAI)
Felix Kares
Timo Speith
Hanwei Zhang
Markus Langer
FAttXAI
393
4
0
23 Apr 2025
Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods
Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods
Andres Fernandez
Frank Schneider
Maren Mahsereci
Philipp Hennig
347
1
0
20 Apr 2025
PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks
PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks
Huzaifa Arif
K. Murugesan
Payel Das
Alex Gittens
Pin-Yu Chen
AAML
263
0
0
08 Apr 2025
A Comparative Study of Explainable AI Methods: Model-Agnostic vs. Model-Specific Approaches
A Comparative Study of Explainable AI Methods: Model-Agnostic vs. Model-Specific Approaches
Keerthi Devireddy
120
5
0
05 Apr 2025
ESC: Erasing Space Concept for Knowledge Deletion
ESC: Erasing Space Concept for Knowledge DeletionComputer Vision and Pattern Recognition (CVPR), 2025
Tae-Young Lee
Sundong Park
M. Jeon
Hyoseok Hwang
Gyeong-Moon Park
KELMMU
286
0
0
03 Apr 2025
Fast Fourier Correlation is a Highly Efficient and Accurate Feature Attribution Algorithm from the Perspective of Control Theory and Game Theory
Fast Fourier Correlation is a Highly Efficient and Accurate Feature Attribution Algorithm from the Perspective of Control Theory and Game Theory
Zechen Liu
Feiyang Zhang
Wei Song
Xuelong Li
Wei Wei
FAtt
414
0
0
02 Apr 2025
Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimators
Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimators
Teodor Chiaburu
Felix Bießmann
Frank Haußer
215
3
0
01 Apr 2025
VITAL: More Understandable Feature Visualization through Distribution Alignment and Relevant Information Flow
VITAL: More Understandable Feature Visualization through Distribution Alignment and Relevant Information Flow
Ada Gorgun
Bernt Schiele
Jonas Fischer
209
1
0
28 Mar 2025
Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
Bowei Tian
Xuntao Lyu
Meng Liu
Hongyi Wang
Ang Li
160
3
0
25 Mar 2025
Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning
Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning
Nhi Pham
Bernt Schiele
Bernt Schiele
Adam Kortylewski
Jonas Fischer
302
0
0
17 Mar 2025
Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspective
Guanhua Zheng
Jitao Sang
Changsheng Xu
AAMLFAtt
316
0
0
14 Mar 2025
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning TasksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Eduard Tulchinskii
Daria Voronkova
I. Trofimov
Evgeny Burnaev
Serguei Barannikov
883
1
0
14 Mar 2025
Axiomatic Explainer Globalness via Optimal Transport
Axiomatic Explainer Globalness via Optimal TransportInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Davin Hill
Josh Bone
A. Masoomi
Max Torop
Jennifer Dy
505
2
0
13 Mar 2025
How Does Overparameterization Affect Machine Unlearning of Deep Neural Networks?
Gal Alon
Yehuda Dar
MUAI4CE
166
3
0
11 Mar 2025
Now you see me! Attribution Distributions Reveal What is Truly Important for a Prediction
Now you see me! Attribution Distributions Reveal What is Truly Important for a Prediction
Nils Philipp Walter
Jilles Vreeken
Jonas Fischer
FAtt
328
0
0
10 Mar 2025
Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations
Eren Erogullari
Sebastian Lapuschkin
Wojciech Samek
Frederik Pahde
LLMSVCoGe
250
1
0
07 Mar 2025
ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
Rikuto Kotoge
Ziwei Yang
Zheng Chen
Yushun Dong
Yasuko Matsubara
Jimeng Sun
Yasushi Sakurai
269
0
0
25 Feb 2025
LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint
LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-DisjointAnnual Meeting of the Association for Computational Linguistics (ACL), 2025
Qianli Ma
Dongrui Liu
Qian Chen
Linfeng Zhang
Jing Shao
MoMe
986
4
0
24 Feb 2025
A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
Wei Liu
Muhammad Khan
Gabriel Mancino-Ball
Yangyang Xu
237
3
0
24 Feb 2025
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional InteractionsInternational Conference on Learning Representations (ICLR), 2025
Tue Cao
Nhat X. Hoang
Hieu H. Pham
P. Nguyen
My T. Thai
535
2
0
22 Feb 2025
Revisiting the Generalization Problem of Low-level Vision Models Through the Lens of Image Deraining
Jinfan Hu
Zhiyuan You
Jinjin Gu
Kaiwen Zhu
Tianfan Xue
Chao Dong
442
2
0
18 Feb 2025
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
Thomas Fel
Ekdeep Singh Lubana
Jacob S. Prince
M. Kowal
Victor Boutin
Isabel Papadimitriou
Binxu Wang
Martin Wattenberg
Demba Ba
Talia Konkle
299
28
0
18 Feb 2025
B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability
B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability
Yifan Wang
Sukrut Rao
Ji-Ung Lee
Mayank Jobanputra
Vera Demberg
232
0
0
18 Feb 2025
Explaining 3D Computed Tomography Classifiers with Counterfactuals
Explaining 3D Computed Tomography Classifiers with Counterfactuals
Joseph Paul Cohen
Louis Blankemeier
Akshay S. Chaudhari
MedIm
1.1K
1
0
11 Feb 2025
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
Harrish Thasarathan
Julian Forsyth
Thomas Fel
M. Kowal
Konstantinos G. Derpanis
345
24
0
06 Feb 2025
Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis
Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis
Haowen Bai
Zixiang Zhao
Jiangshe Zhang
Baisong Jiang
Lilun Deng
Yukun Cui
Shuang Xu
Chunxia Zhang
399
16
0
03 Feb 2025
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
B-cosification: Transforming Deep Neural Networks to be Inherently InterpretableNeural Information Processing Systems (NeurIPS), 2024
Shreyash Arya
Sukrut Rao
Moritz Bohle
Bernt Schiele
364
10
0
28 Jan 2025
Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification
Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification
C. Tinauer
Maximilian Sackl
Rudolf Stollberger
Reinhold Schmidt
Stefan Ropele
C. Langkammer
AAML
253
1
0
27 Jan 2025
Generating visual explanations from deep networks using implicit neural representations
Generating visual explanations from deep networks using implicit neural representationsIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2025
Michal Byra
Henrik Skibbe
GANFAtt
230
2
0
20 Jan 2025
An Interpretable Neural Control Network with Adaptable Online Learning for Sample Efficient Robot Locomotion Learning
An Interpretable Neural Control Network with Adaptable Online Learning for Sample Efficient Robot Locomotion LearningIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2025
Arthicha Srisuchinnawong
Poramate Manoonpong
160
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A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityEURASIP Journal on Information Security (EURASIP J. Inf. Secur.), 2025
Osvaldo Arreche
Mustafa Abdallah
AAML
430
7
0
14 Jan 2025
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and MetricsNeural Information Processing Systems (NeurIPS), 2024
Lukas Klein
Carsten T. Lüth
U. Schlegel
Till J. Bungert
Mennatallah El-Assady
Paul F. Jäger
XAIELM
648
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03 Jan 2025
Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers
Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers
Bohang Sun
Pietro Liò
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440
1
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NADER: Neural Architecture Design via Multi-Agent Collaboration
NADER: Neural Architecture Design via Multi-Agent CollaborationComputer Vision and Pattern Recognition (CVPR), 2024
Zekang Yang
Wang Zeng
Sheng Jin
Chen Qian
Ping Luo
Wentao Liu
AI4CE
120
0
0
26 Dec 2024
Can Input Attributions Explain Inductive Reasoning in In-Context Learning?
Can Input Attributions Explain Inductive Reasoning in In-Context Learning?Annual Meeting of the Association for Computational Linguistics (ACL), 2024
Mengyu Ye
Tatsuki Kuribayashi
Goro Kobayashi
Jun Suzuki
LRM
439
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0
20 Dec 2024
Toward Efficient Data-Free Unlearning
Toward Efficient Data-Free UnlearningAAAI Conference on Artificial Intelligence (AAAI), 2024
Chenhao Zhang
Shaofei Shen
Weitong Chen
Miao Xu
MU
356
3
0
18 Dec 2024
From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation
From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation
Kristoffer Wickstrøm
Marina M.-C. Höhne
Anna Hedström
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395
5
0
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Explaining Object Detectors via Collective Contribution of Pixels
Explaining Object Detectors via Collective Contribution of Pixels
Toshinori Yamauchi
Hiroshi Kera
K. Kawamoto
ObjDFAtt
555
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Explaining the Impact of Training on Vision Models via Activation Clustering
Explaining the Impact of Training on Vision Models via Activation Clustering
Ahcène Boubekki
Samuel G. Fadel
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707
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Large Scale Evaluation of Deep Learning-based Explainable Solar Flare
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Explainable AI Approach using Near Misses Analysis
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LibraGrad: Balancing Gradient Flow for Universally Better Vision
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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based
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