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Title: Revisit Recurrent Attention Model from an Active Sampling Perspective

Paper content:
We revisit the Recurrent Attention Model (RAM, Mnih et al. (2014)), a recurrent neural network for visual attention, from an active information sampling perspective.
We borrow ideas from neuroscience research on the role of active information sampling in the context of visual attention and gaze (Gottlieb, 2018), where the author suggested three types of motives for active information sampling strategies.
We find the original RAM model only implements one of them.
We identify three key weakness of the original RAM and provide a simple solution by adding two extra terms on the objective function.
The modified RAM
1) achieves faster convergence,
2) allows dynamic decision making per sample without loss of accuracy, and
3) generalizes much better on longer sequence of glimpses which is not trained for, compared with the original RAM.
We revisit the Recurrent Attention Model (RAM, ), a recurrent neural network for visual attention, from an active information sampling perspective.
The RAM, instead of processing the input image for classification in full, only takes a glimpse at a small patch of the image at a time.
The recurrent attention mechanism learns where to look at to obtain new information based on the internal state of the network.
After a pre-defined number of glimpses, RAM finally makes a prediction as output.
Compared with the attention mechanism which now dominates the AI/NLP research such as Transformer (Vaswani et al., 2017) and BERT (Devlin et al., 2018) , this recurrent attention mechanism is fundamentally different, as it is used to obtain new information (active sampling of information), rather than processing information that is already fully observed.
In this paper, we identify three weaknesses of this widely-cited approach.
First, the convergence of RAM training is slow.
Second, RAM does not support dynamic number of glimpses per sample, but uses a fixed number of glimpses for every sample.
Third and perhaps most importantly, the performance of the original RAM does not improve but rather decrease dramatically if it takes more glimpses, which is weird and against intuition.
We provide a simple solution of adding two extra terms in the objective function of RAM, insipred from neuroscience research (Gottlieb, 2018) which discusses the logic and neural substrates of information sampling policies in the context of visual attention and gaze.
Base on the evidence available so far, Gottlieb (2018) suggested three kinds of motives for the active sampling strategies of decision-making while the original RAM only implements one of them.
We incorporate the other two motives in the objective function, and by doing so we
1) achieve much faster convergence and
2) instantly enbale decision making with a dynamic number of glimpse for different samples with no loss of accuracy.
3) More importantly, we find that the modified RAM generalizes much better to longer sequence of glimpses which is not trained for.
We evaluate on MNIST dataset as in the orignal RAM paper.
We set the train-time number of glimpses N = 6 for it achieves the best test-time accuracy in .
Implementation details see the source code 1 .
We first show in Figure 1 that the two new terms in the objective both contribute to a faster convergence.
We test four cases
1) the orignal objective,
2) add the J intrinsic , 3) add J uncertainty , 4) add both new terms.
We see in Figure 1 that both of our new objective in isolation help a faster learning and together give the fastest convergence.
As in Figure 2 , we test the trained models with varying number of glimpses.
(We want to emphasize that the focus is not the absolute performance , but rather the generalization on more glimpses than train time.)
We fisrt evaluate the non-dynamic case (fixed number for all samples).
The performance of the original RAM decrease dramatically when N > 10.
Adding both terms, the modified RAM does not suffer the decrease anymore even when N is large .
Also, it is interesting that adding only the uncertainty term, we observe the improvement is very slight and the intrinsic term effectively stablizes the prediction accuracy given more glimpses.
We also test the dynamic case by varying the exploration rate.
We see that dynamic number of glimpses does not hurt the performance very much, which confirms with the hypothesis that some samples are easier to discriminate and thus need fewer glimpses.
One may argue that the given longer training time or other hyperparameter tuning, RAM will eventually reach a point where it can give stable prediction accuracy on more glimpses, and the new objective only make it converge faster to that point.
But during our experiments, we find with λ 2 = 0.1 the J intrinsic term can effectively stablize the prediction given more glimpses, even when trained for only 1 epoch.
We observe that the l2-norm of internal states of orginal RAM becomes very large given a longer sequence of glimpses while the modified RAM with J intrinsic remains stable.

assistant: text: Inspired by neuroscience research, solve three key weakness of the widely-cited recurrent attention model by simply adding two terms on the objective function.