Tensorflow Implementation of Recurrent Attention Model (RAM)

Author

Juntae, Kim, jtkim@kaist.ac.kr

Requirement

tensorflow rc 1.1.0-rc0

Description

code: 'ram_modified.py'

This project is modified version of https://github.com/jlindsey15/RAM. The critical problem of last implemetnation is that the location network cannot learn because of tf.stop_gradient implementation so that they got just '94% accuracy'. It seems relatively bad compared to the result of paper. If 'tf.stop_gradient' was commented, the classification result was very bad. The reason I think is that the problem is originated from sharing the gradient flow through location, core, glimpse network. Through gradient sharing, gradients of classification part are corrupted by gradients of reinforcement part so that classification result become very bad. (If someone want to share gradient, the weighted loss should be needed. please refer https://arxiv.org/pdf/1412.7755.pdf) According to their post research, 'Multiple Object Recognition with Visual Attention' (https://arxiv.org/pdf/1412.7755.pdf) they softly separate location network and others through multi-layer RNN. From this, I assume that sharing the gradient through whole network is not a good idea so separate them, and finally got a good result. In summary, the learning stretegy is as follow.

1. location network, baseline network : learn with gradients of reinforcement learning only.

2. glimpse network, core network : learn with gradients of supervised learning only.

Thank you!

Result

After 600,000 epoch, I got about 98% accuracy.

Reference

Recurrent Models of Visual Attention

http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf

https://arxiv.org/pdf/1412.7755.pdf