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- W3045793283 abstract "Over the long history of machine learning, which dates back several decades, recurrent neural networks (RNNs) have been used mainly for sequential data and time series and generally with 1D information. Even in some rare studies on 2D images, these networks are used merely to learn and generate data sequentially rather than for image recognition tasks. In this study, we propose integrating an RNN as an additional layer when designing image recognition models. We also develop end-to-end multimodel ensembles that produce expert predictions using several models. In addition, we extend the training strategy so that our model performs comparably to leading models and can even match the state-of-the-art models on several challenging datasets (e.g., SVHN (0.99), Cifar-100 (0.9027) and Cifar-10 (0.9852)). Moreover, our model sets a new record on the Surrey dataset (0.949). The source code of the methods provided in this article is available at this https URL and this http URL." @default.
- W3045793283 created "2020-08-03" @default.
- W3045793283 creator A5004095778 @default.
- W3045793283 creator A5046882659 @default.
- W3045793283 date "2020-07-30" @default.
- W3045793283 modified "2023-09-27" @default.
- W3045793283 title "Rethinking Recurrent Neural Networks and other Improvements for Image Classification." @default.
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- W3045793283 hasPublicationYear "2020" @default.
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