The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual scenes. Short term memory plays an integral role in aggregating the information obtained from these glimpses and informing our interpretation of the scene. Computational models have attempted to address glimpsing and visual attention but have failed to incorporate the notion of memory. We introduce a novel, biologically inspired visual working memory architecture that we term the Hebb-Rosenblatt memory. We subsequently introduce a fully differentiable Short Term Attentive Working Memory model (STAWM) which uses transformational attention to learn a memory over each image it sees. The state of our HebbRosenblatt memory is embedded in STAWM as the weights space of a layer. By projecting different queries through this layer we can obtain goal-oriented latent representations for tasks including classification and visual reconstruction. Our model obtains highly competitive classification performance on MNIST and CIFAR-10. As demonstrated through the CelebA dataset, to perform reconstruction the model learns to make a sequence of updates to a canvas which constitute a parts-based representation. Classification with the self supervised representation obtained from MNIST is shown to be in line with the state of the art models (none of which use a visual attention mechanism). Finally, we show that STAWM can be trained under the dual constraints of classification and reconstruction to provide an interpretable visual sketchpad which helps open the 'black-box' of deep learning.
Revision:
The authors have took in the feedback from myself and the other reviewers wholeheartedly, and have clearly worked hard to improve the results, and the paper during the revision process. In addition, their code release encourages easy reproducibility of their model, which imo is needed for this work given the non-conventional nature of the model (that being said, the paper itself is well written and the authors have done sufficiently well in explaining their approach, and also the motivation behind it, as per my original review). The code is relatively clear and self-contained demonstrating their experiments on MNIST, CelebA demonstrating the use of the visual sketch model.
I believe the improvements, especially given the compute resources available to the authors, warrant a strong accept of this work, so I revised my score to 9. I also believe this work will be of value to the ICLR community as it offers alternate, less explored approaches compared to methods that are typically used in this domain. I'm excited to see more in the community explore biologically inspired approaches to generative models, and I think this work along with the code base will be an important base for other researchers to use as a reference point for future work.
Original Review below:
Summary: They propose a biologically motivated short term attentive working memory (STAWM) generative model for images. The architecture is based on Hebbian Learning (i.e. associative memories are represented in the weight matrices that are dynamically updated during inference by a modified version of Hebbian learning rule). These memories are sampled from glimpses on an input image (using attention on contextual states, similar to [1]), in addition to a latent, query state. This model learns a representation of images that can be used for sequential reconstruction (via a sequence of updates, like a sketchpad, like DRAW [1], trained in an unsupervised manner). These memories produced by drawing can also be used for semi-supervised classification (achieves very respectable and competitive results for MNIST and CIFAR-10).
This paper is beautifully written, and the biological inspiration, motivation behind this work, and links to neuroscience literature as well as relation to existing ML work (even recent papers) is well stated. The main strength of this paper is that the author went from a biologically inspired idea to a complete realization of the idea in algorithmic form. The semi-supervised classification results are competitive to SOTA, and although the CIFAR-10 reconstruction results are not great (especially compared to generative adversarial models or recent variation models [2]), I think the approach is coming from a very different angle that is different enough compared to the literature to warrant some attention, or at least a glimpse, so to speak, from the broader community. The method may offer new ways to interpret ML models that is current models lack, which in itself is an important contribution. That being said, the fact that most adversarial generative models achieved a far better performance raises concern on the generalization ability of these memory-inspired learned representations, and I look forward to seeing future work investigate this area in more detail.
The authors also took great care in writing details for important parts of the experiments in the Appendix section, and open sourced the implementation to reproduce all their experiments. Given the complex nature of this model, they did a great job in writing a clear explanation, and provided enough details for the community to build biologically inspired models for deep networks. Even without the code, I felt I might have been able to implement most of the model given the detail and clarity of the writing, so having both available is a great contribution.
I highly recommend this paper for acceptance, with a score of 8 (edit: revised to 9 after rebuttal period). The paper might warrant a score of 9 if they had also achieved higher quality results for image generation, on Celeb-A or demonstrated results on ImageNet, and provided more detailed analysis about drawbacks of their approach vs conventional generative models.
[1] https://arxiv.org/abs/1502.04623
[2] https://arxiv.org/abs/1807.03039