Tensorflow implementation of Recurrent Models of Visual Attention
Python
Tensorflow implementation of Recurrent Models of Visual Attention (Mnih et al. 2014), with additional research. Code based off of https://github.com/zhongwen/RAM.
Results
60 by 60 Translated MNIST
Model
Error
FC, 2 layers (64 hiddens each)
6.78%
FC, 2 layers (256 hiddens each)
2.65%
Convolutional, 2 layers
1.57%
RAM, 4 glimpses, 12 x 12, 3 scale
1.54%
RAM, 6 glimpses, 12 x 12, 3 scale
1.08%
RAM, 8 glimpses, 12 x 12, 3 scale
0.94%
60 by 60 Cluttered Translated MNIST
Model
Error
FC, 2 layers (64 hiddens each)
29.13%
FC, 2 layers (256 hiddens each)
11.36%
Convolutional, 2 layers
8.37%
RAM, 4 glimpses, 12 x 12, 3 scale
5.15%
RAM, 6 glimpses, 12 x 12, 3 scale
3.33%
RAM, 8 glimpses, 12 x 12, 3 scale
2.63%
100 by 100 Cluttered Translated MNIST
Model
Error
Convolutional, 2 layers
16.22%
RAM, 4 glimpses, 12 x 12, 3 scale
14.86%
RAM, 6 glimpses, 12 x 12, 3 scale
8.3%
RAM, 8 glimpses, 12 x 12, 3 scale
5.9%
60 by 60 Cluttered MNIST 6 Glimpses Examples
Solid square is first glimpse, line is path of attention, circle is last glimpse.
