Pruning by explaining: A novel criterion for deep neural network pruning

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

(2021) Wojciech Samek et al.   PROCEEDINGS OF THE IEEE

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

(2020) Miriam Hägele et al.   Scientific Reports

Unmasking Clever Hans predictors and assessing what machines really learn

(2019) Sebastian Lapuschkin et al.   Nature Communications

NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm

(2019) Xiaoliang Dai et al.   IEEE TRANSACTIONS ON COMPUTERS

Transfer channel pruning for compressing deep domain adaptation models

(2019) Chaohui Yu et al.   International Journal of Machine Learning and Cybernetics

Compressing the CNN architecture for in-air handwritten Chinese character recognition

(2019) Ji Gan et al.   PATTERN RECOGNITION LETTERS

Compact and Computationally Efficient Representation of Deep Neural Networks

(2019) Simon Wiedemann et al.   IEEE Transactions on Neural Networks and Learning Systems

Methods for interpreting and understanding deep neural networks

(2018) Grégoire Montavon et al.   DIGITAL SIGNAL PROCESSING

Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges

(2018) Yu Cheng et al.   IEEE SIGNAL PROCESSING MAGAZINE

ThiNet: Pruning CNN Filters for a Thinner Net

(2018) Jian-Hao Luo et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Recent advances in convolutional neural networks

(2018) Jiuxiang Gu et al.   PATTERN RECOGNITION

Channel pruning based on mean gradient for accelerating Convolutional Neural Networks

(2018) Congcong Liu et al.   SIGNAL PROCESSING

LightweightNet: Toward fast and lightweight convolutional neural networks via architecture distillation

(2018) Ting-Bing Xu et al.   PATTERN RECOGNITION

Deep Neural Network Compression by In-Parallel Pruning-Quantization

(2018) Frederick Tung et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Explaining nonlinear classification decisions with deep Taylor decomposition

(2017) Grégoire Montavon et al.   PATTERN RECOGNITION

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

(2017) Vivienne Sze et al.   PROCEEDINGS OF THE IEEE

Evaluating the Visualization of What a Deep Neural Network Has Learned

(2017) Wojciech Samek et al.   IEEE Transactions on Neural Networks and Learning Systems

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

(2015) Sebastian Bach et al.   PLoS One