Recurrent Visual Attention

This is a PyTorch implementation of Recurrent Models of Visual Attention by Volodymyr Mnih, Nicolas Heess, Alex Graves and Koray Kavukcuoglu.

The Recurrent Attention Model (RAM) is a neural network that processes inputs sequentially, attending to different locations within the image one at a time, and incrementally combining information from these fixations to build up a dynamic internal representation of the image.

Model Description

In this paper, the attention problem is modeled as the sequential decision process of a goal-directed agent interacting with a visual environment. The agent is built around a recurrent neural network: at each time step, it processes the sensor data, integrates information over time, and chooses how to act and how to deploy its sensor at the next time step.

• glimpse sensor: a retina that extracts a foveated glimpse phi around location l from an image x. It encodes the region around l at a high-resolution but uses a progressively lower resolution for pixels further from l, resulting in a compressed representation of the original image x.
• glimpse network: a network that combines the "what" (phi) and the "where" (l) into a glimpse feature vector wg_t.
• core network: an RNN that maintains an internal state that integrates information extracted from the history of past observations. It encodes the agent's knowledge of the environment through a state vector h_t that gets updated at every time step t.
• location network: uses the internal state h_t of the core network to produce the location coordinates l_t for the next time step.
• action network: after a fixed number of time steps, uses the internal state h_t of the core network to produce the final output classification y.

Results

I decided to tackle the 28x28 MNIST task with the RAM model containing 6 glimpses, of size 8x8, with a scale factor of 1.

Model	Validation Error	Test Error
6 8x8	1.1	1.21

I haven't done random search on the policy standard deviation to tune it, so I expect the test error can be reduced to sub 1% error. I'll be updating the table above with results for the 60x60 Translated MNIST, 60x60 Cluttered Translated MNIST and the new Fashion MNIST dataset when I get the time.

Finally, here's an animation showing the glimpses extracted by the network on a random batch at epoch 23.

With the Adam optimizer, paper accuracy can be reached in ~160 epochs.

Usage

The easiest way to start training your RAM variant is to edit the parameters in config.py and run the following command:

python main.py

To resume training, run:

python main.py --resume=True

Finally, to test a checkpoint of your model that has achieved the best validation accuracy, run the following command:

python main.py --is_train=False

References

• Torch Blog Post on RAM