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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
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Papers citing
"Striving for Simplicity: The All Convolutional Net"
50 / 1,917 papers shown
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Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
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Towards Interpretable Attention Networks for Cervical Cancer Analysis
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Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey
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GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection
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Automatic Fault Detection for Deep Learning Programs Using Graph Transformations
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A Review on Explainability in Multimodal Deep Neural Nets
IEEE Access (IEEE Access), 2021
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How to Explain Neural Networks: an Approximation Perspective
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A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
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Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks
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Majid Komeili
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194
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Sanity Simulations for Saliency Methods
International Conference on Machine Learning (ICML), 2021
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Gregory Plumb
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Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
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Canonical Saliency Maps: Decoding Deep Face Models
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S. M. I. C. V. Balasubramanian
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Adversarial Example Detection for DNN Models: A Review and Experimental Comparison
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Olivier Déforges
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01 May 2021
Interpretable Semantic Photo Geolocation
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Deep Transform and Metric Learning Networks
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Émilie Chouzenoux
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326
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20 Apr 2021
Interpreting intermediate convolutional layers of generative CNNs trained on waveforms
IEEE/ACM Transactions on Audio Speech and Language Processing (TASLP), 2021
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Improving Attribution Methods by Learning Submodular Functions
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324
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Knowledge Neurons in Pretrained Transformers
Annual Meeting of the Association for Computational Linguistics (ACL), 2021
Damai Dai
Li Dong
Y. Hao
Zhifang Sui
Baobao Chang
Furu Wei
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Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained Models
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Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
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181
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Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation
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LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information
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Epigenetic evolution of deep convolutional models
IEEE Congress on Evolutionary Computation (CEC), 2019
Alexander Hadjiivanov
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VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
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153
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White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks
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Explainability-aided Domain Generalization for Image Classification
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Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
International Journal of Applied Earth Observation and Geoinformation (JAEOG), 2021
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206
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LiftPool: Bidirectional ConvNet Pooling
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Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Blaž Bertalanič
Marko Meza
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Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021
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Yang Zhang
W. Mansour
Yuezhi Cai
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271
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NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization
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Tien-Ju Yang
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Knowledge Distillation By Sparse Representation Matching
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Neural Response Interpretation through the Lens of Critical Pathways
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Nassir Navab
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Wave based damage detection in solid structures using artificial neural networks
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Hao Lyu
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Z. Rizvi
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MISA: Online Defense of Trojaned Models using Misattributions
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269
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Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation
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169
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Yi Sun
Abel N. Valente
Sijia Liu
Dakuo Wang
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182
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Qing-Long Zhang
Lu Rao
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227
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ECINN: Efficient Counterfactuals from Invertible Neural Networks
British Machine Vision Conference (BMVC), 2021
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