Rethinking Recurrent Neural Networks and other Improvements for Image Classification

07/30/2020 ∙ by Nguyen Huu Phong, et al. ∙ 0

For a long history of Machine Learning which dates back to several decades, Recurrent Neural Networks (RNNs) have been mainly used for sequential data and time series or generally 1D information. Even in some rare researches on 2D images, the networks merely learn and generate data sequentially rather than for recognition of images. In this research, we propose to integrate RNN as an additional layer in designing image recognition's models. Moreover, we develop End-to-End Ensemble Multi-models that are able to learn experts' predictions from several models. Besides, we extend training strategy and softmax pruning which overall leads our designs to perform comparably to top models on several datasets. The source code of the methods provided in this article is available in https://github.com/leonlha/e2e-3m and http://nguyenhuuphong.me.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 6

page 8

page 9

page 10

page 11

page 14

page 15

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.