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Striving for Simplicity: The All Convolutional Net

21 December 2014

Jost Tobias Springenberg

Alexey Dosovitskiy

Thomas Brox

Martin Riedmiller

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

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Title
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling W. Jiang Gang Liu Xiaofeng Chen Yipeng Zhou FedML 28 0 0 30 Dec 2022
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN I. A. Showmik Tahsina Farah Sanam H. Imtiaz 31 15 0 26 Dec 2022
Explainable AI for Bioinformatics: Methods, Tools, and Applications Md. Rezaul Karim Tanhim Islam Oya Beyan Christoph Lange Michael Cochez Dietrich-Rebholz Schuhmann Stefan Decker 114 78 0 25 Dec 2022
DExT: Detector Explanation Toolkit Deepan Padmanabhan Paul G. Plöger Octavio Arriaga Matias Valdenegro-Toro 69 2 0 21 Dec 2022
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study Vincenzo Riccio Paolo Tonella AAML 56 29 0 21 Dec 2022
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint Borui Zhang Wenzhao Zheng Jie Zhou Jiwen Lu AAML 88 7 0 18 Dec 2022
Domain Generalization by Learning and Removing Domain-specific Features Yuzhu Ding Lei Wang Binxin Liang Shuming Liang Yang Wang Fangxiao Chen OOD 95 42 0 14 Dec 2022
Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods Ming-Xiu Jiang Saeed Khorram Li Fuxin FAtt 110 11 0 13 Dec 2022
Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data Utku Ozbulak Solha Kang Jasper Zuallaert Stephen Depuydt J. Vankerschaver 28 0 0 12 Dec 2022
Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation Julius Adebayo M. Muelly H. Abelson Been Kim 87 87 0 09 Dec 2022
COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data Jonas C. Ditz Bernhard Reuter Nícolas Pfeifer 53 1 0 02 Dec 2022
Optimizing Explanations by Network Canonization and Hyperparameter Search Frederik Pahde Galip Umit Yolcu Alexander Binder Wojciech Samek Sebastian Lapuschkin 119 11 0 30 Nov 2022
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning Young Geun Kim Carole-Jean Wu FedML 88 5 0 30 Nov 2022
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning Eldor Abdukhamidov Mohammed Abuhamad Simon S. Woo Eric Chan-Tin Tamer Abuhmed AAML 63 9 0 29 Nov 2022
Towards More Robust Interpretation via Local Gradient Alignment Sunghwan Joo Seokhyeon Jeong Juyeon Heo Adrian Weller Taesup Moon FAtt 83 6 0 29 Nov 2022
Foiling Explanations in Deep Neural Networks Snir Vitrack Tamam Raz Lapid Moshe Sipper AAML 75 17 0 27 Nov 2022
Attribution-based XAI Methods in Computer Vision: A Review Kumar Abhishek Deeksha Kamath 67 21 0 27 Nov 2022
Looking at the posterior: accuracy and uncertainty of neural-network predictions Hampus Linander Oleksandr Balabanov Henry Yang Bernhard Mehlig UQCV UD BDL 80 2 0 26 Nov 2022
Evaluating Feature Attribution Methods for Electrocardiogram J. Suh Jimyeong Kim Euna Jung Wonjong Rhee FAtt 45 2 0 23 Nov 2022
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations Alexander Binder Leander Weber Sebastian Lapuschkin G. Montavon Klaus-Robert Muller Wojciech Samek FAtt AAML 65 23 0 22 Nov 2022
Explaining Image Classifiers with Multiscale Directional Image Representation Stefan Kolek Robert Windesheim Héctor Andrade-Loarca Gitta Kutyniok Ron Levie 58 5 0 22 Nov 2022
CRAFT: Concept Recursive Activation FacTorization for Explainability Thomas Fel Agustin Picard Louis Bethune Thibaut Boissin David Vigouroux Julien Colin Rémi Cadène Thomas Serre 105 116 0 17 Nov 2022
Parameter-Efficient Transformer with Hybrid Axial-Attention for Medical Image Segmentation Yiyue Hu Lei Zhang Nan Mu Leijun Liu ViT MedIm 44 1 0 17 Nov 2022
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine Ahmad Chaddad Qizong Lu Jiali Li Y. Katib R. Kateb C. Tanougast Ahmed Bouridane Ahmed Abdulkadir OOD 72 38 0 17 Nov 2022
Explaining Cross-Domain Recognition with Interpretable Deep Classifier Yiheng Zhang Ting Yao Zhaofan Qiu Tao Mei OOD 70 3 0 15 Nov 2022
A Rigorous Study Of The Deep Taylor Decomposition Leon Sixt Tim Landgraf FAtt AAML 46 4 0 14 Nov 2022
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Zixi Chen Varshini Subhash Marton Havasi Weiwei Pan Finale Doshi-Velez XAI FAtt 119 19 0 10 Nov 2022
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 82 4 0 09 Nov 2022
Privacy Meets Explainability: A Comprehensive Impact Benchmark S. Saifullah Dominique Mercier Adriano Lucieri Andreas Dengel Sheraz Ahmed 62 14 0 08 Nov 2022
Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust Haotian Chen Lingwei Zhang Yiran Liu Fanchao Chen Yang Yu AILaw ELM 49 6 0 06 Nov 2022
Exploring Explainability Methods for Graph Neural Networks Harsh Patel Shivam Sahni 32 0 0 03 Nov 2022
Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data Atif Khan C. Lawless Amy Vincent Satish Pilla S. Ramesh A. Mcgough 66 0 0 31 Oct 2022
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks Yong-Min Shin Sun-Woo Kim Won-Yong Shin 82 6 0 31 Oct 2022
Spectrograms Are Sequences of Patches Leyi Zhao Yi Li SSL 57 0 0 28 Oct 2022
Explaining the Explainers in Graph Neural Networks: a Comparative Study Antonio Longa Steve Azzolin G. Santin G. Cencetti Pietro Lio Bruno Lepri Andrea Passerini 107 31 0 27 Oct 2022
HesScale: Scalable Computation of Hessian Diagonals Mohamed Elsayed A. R. Mahmood 82 8 0 20 Oct 2022
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection Sunsheng Gu Vahdat Abdelzad Krzysztof Czarnecki 68 1 0 20 Oct 2022
Similarity of Neural Architectures using Adversarial Attack Transferability Ian Ryu Dongyoon Han Byeongho Heo Song Park Sanghyuk Chun Jong-Seok Lee AAML 132 2 0 20 Oct 2022
Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks Ankit Gupta I. Sintorn HAI 100 1 0 20 Oct 2022
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching Cangxiong Chen Neill D. F. Campbell FedML 55 1 0 20 Oct 2022
Gradient Backpropagation based Feature Attribution to Enable Explainable-AI on the Edge Ashwin Bhat A. S. Assoa A. Raychowdhury 64 9 0 19 Oct 2022
ATCON: Attention Consistency for Vision Models Ali Mirzazadeh Florian Dubost M. Pike Krish Maniar Max Zuo Christopher Lee-Messer D. Rubin 28 1 0 18 Oct 2022
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text Betty van Aken Jens-Michalis Papaioannou M. Naik G. Eleftheriadis Wolfgang Nejdl Felix Alexander Gers Alexander Loser 86 11 0 16 Oct 2022
A Survey on Explainable Anomaly Detection Zhong Li Yuxuan Zhu M. Leeuwen 115 79 0 13 Oct 2022
Toward the application of XAI methods in EEG-based systems Andrea Apicella Francesco Isgrò A. Pollastro R. Prevete OOD AI4TS 57 15 0 12 Oct 2022
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning Tao Yang Jinghao Deng Xiaojun Quan Qifan Wang Shaoliang Nie 78 4 0 12 Oct 2022
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes Peter Kocsis Peter Súkeník Guillem Brasó Matthias Nießner Laura Leal-Taixé Ismail Elezi 52 7 0 11 Oct 2022
Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors Federico Baldassarre Quentin Debard Gonzalo Fiz Pontiveros Tri Kurniawan Wijaya 82 4 0 07 Oct 2022
Critical Learning Periods for Multisensory Integration in Deep Networks Michael Kleinman Alessandro Achille Stefano Soatto 116 11 0 06 Oct 2022
MAtt: A Manifold Attention Network for EEG Decoding Yue Pan Jing-Lun Chou Chunshan Wei 68 43 0 05 Oct 2022