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We propose a modularization method that decomposes a deep neural network (DNN) into small modules from a functionality perspective and recomposes them into a new model for some other task.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
McCloskey, M.; and Cohen, N. J. 1989 · 1989
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Efficient classification for multiclass problems using modular neural networks
Anand, R.; Mehrotra, K.; Mohan, C. K.; and Ranka, S. 1995 · 1995
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Gradient-Based Learning Applied to Document Recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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Modular neural networks: a survey
Auda, G.; and Kamel, M. 1999 · 1999
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Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
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Reading Digits in Natural Images with Unsupervised Feature Learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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Network In Network
Lin, M.; Chen, Q.; and Yan, S. 2014 · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K.; and Zisserman, A. 2015 · 2015
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Convolutional neural networks with low-rank regularization
Tai, C.; Xiao, T.; Wang, X.; and E, W. 2016 · 2016
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Zagoruyko, S.; and Komodakis, N. 2016 · 2016
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
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Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
Mallya, A.; Davis, D.; and Lazebnik, S. 2018 · 2018
Cited alongside, same era.
Modular representation of layered neural networks
Watanabe, C.; Hiramatsu, K.; and Kashino, K. 2018 · 2018
Investigating the Compositional Structure Of Deep Neural Networks
Craighero, F.; Angaroni, F.; Graudenzi, A.; Stella, F.; and Antoniotti, M. 2020 · 2020
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RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
Cubuk, E. D.; Zoph, B.; Shlens, J.; and Le, Q. 2020 · 2020
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Neural Networks are Surprisingly Modular
Filan, D.; Hod, S.; Wild, C.; Critch, A.; and Russell, S. 2020 · 2020
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Compositionality Decomposed: How do Neural Networks Generalise? (Extended Abstract)
Hupkes, D.; Dankers, V.; Mul, M.; and Bruni, E. 2020 · 2020
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On decomposing a deep neural network into modules
Pan, R.; and Rajan, H. 2020 · 2020
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What’s Hidden in a Randomly Weighted Neural Network?
Ramanujan, V.; Wortsman, M.; Kembhavi, A.; Farhadi, A.; and Rastegari, M. 2020 · 2020
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A review of modularization techniques in artificial neural networks
Amer, M.; and Maul, T. 2019 · 2019
Cited alongside, same era.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J.; and Carbin, M. 2019 · 2019
Cited alongside, same era.
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Zhou, H.; Lan, J.; Liu, R.; and Yosinski, J. 2019 · 2019
Cited alongside, same era.
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Supermasks in Superposition
Wortsman, M.; Ramanujan, V.; Liu, R.; Kembhavi, A.; Rastegari, M.; Yosinski, J.; and Farhadi, A. 2020 · 2020
Later among the works it cites.
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
Csordás, R.; van Steenkiste, S.; and Schmidhuber, J. 2021 · 2021
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