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Bridging the Gaps Between Residual Learn...

Liao, Qianli...

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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex by Liao, Qianli ( Author )

Content Provider	Lemonjar Open Access
Publisher	Australian National University
Publication Date	08-08-2023
Summary	We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a special type of shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 and ImageNet dataset.
ISBN	-
Item Type	Article
File Type	pdf
File Size	29.34 KB
Language	English
Subjects	-
Unlimited	MYR 0.01
Total read	-
URL	http://arxiv.org/abs/1604.03640

Citation

Review the full bibliography data and make any necessary corrections before using. Always consult your library resources for the exact formatting and punctuation guidelines.

AMA 11th EditionReferences

Liao, Qianli. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. Australian National University. August 08, 2023. Accessed October 02, 2025. https://online.provdatabase.com/cite/13004

APA 7th EditionReferences

Liao, Qianli. (2023). Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. Australian National University. https://online.provdatabase.com/cite/13004

Chicago 17th EditionReferences

Liao, Qianli. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. Australian National University. 2023. https://online.provdatabase.com/cite/13004

HarvardReferences

Liao, Qianli. (2023). Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. [Online]. Australian National University. Available from: https://online.provdatabase.com/cite/13004. (Accessed 02 October 2025).

Harvard AustraliaReferences

Liao, Qianli, 2023, Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex, Australian National University, viewed 02 October 2025, <https://online.provdatabase.com/cite/13004>

MLA 9th EditionWorks Cited

Liao, Qianli. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. Australian National University, 2023. PROV Database, https://online.provdatabase.com/cite/13004.

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