Home > Uncategorized > Weekly QuEST Discussion Topics and News, 8 Apr

Weekly QuEST Discussion Topics and News, 8 Apr

QuEST 8 April 2016

This week has been spent working on a new QuEST framework.  The motivation for the twist is associated with some of our colleagues making advances in ‘generative’ deep learning models.  Most of the QuEST discussions have been associated with discriminative models, other than our recent discussion of the DRAW (Google) system.

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.  Recall that DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images.

The question we want to discuss is the idea of a single cognitive system versus a dual (Stanovich/Evans) framework.  In the new proposed formulation the role of consciousness is NOT to provide a second deliberative cognitive system but to complement / infer bottom up (BU) sensory data and processing done in Sys1 (subconscious) with additional data at the appropriate level of abstraction.

Such a formalism will answer many of the concerns of some of our QuEST colleagues that advocate a very powerful Sys1 (example Robert P).  From an engineering perspective we want to discuss some staged experiments that could investigate the potential power of the new approach using generative models for the ‘conscious’ augmentation of sensory BU processing.

The first experiment will be by the AFRL conscious content curation team – AC3 – they will attempt to use the idea on a couple of examples of caption generation where we currently are making unacceptable captions, examples where the current deep learning system fails.  The Question becomes ‘is there a generative augmentation that could in a couple of understood examples – would result in a more acceptable caption?’

After we do that the next experiments will be what we will call ‘Deep Sleep learning’ or maybe ‘Deep Dreaming Learning NN’.  The idea is we let generative models augment the learning from training data.  This simulates a role dreaming may play.  The training with the BU stages being the outputs of our generative models mimics this idea.  The question becomes would deep learning solutions having been allowed to dream do better on ‘unexpected’ queries, data that was NOT from the training set in terms of composition of training set linguistic expressions.  I would envision training using our current best deep learning algorithms, then allowing the system to go off-line and dream where the stages are provided generative model data to learn from.  Then back to the training data …  The composition of the dreams will be generative model constructed ‘realities’ ~ dreams.

Lastly the experiment we envision is a framework where in-use, when there is a query that will allow the online intervention of the ‘conscious’ augmentation (think of the Hammond formalism on dimensionality, time to respond, …), we have the conscious top-down (TD) conditioning of the BU subconscious Sys1 deliberation.

We have two articles on generative models we want to discuss this week that are the current best ones we’ve reviewed for use in the conscious TD framework – both related to generative adversarial networks:

Deep Generative Image Models using a
Laplacian Pyramid of Adversarial Networks
Denton ( Courant Institute), et al (Facebook)

arXiv:1506.05751v1 [cs.CV] 18 Jun 2015

  • In this paper we introduce a generative parametric model capable of producing high quality samples of natural images.
  • Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion.
  • At each level of the pyramid, a separate generative convent model is trained using the Generative Adversarial Nets (GAN) approach [10].
  • Samples drawn from our model are of significantly higher quality than alternate approaches.
  • In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model.
  • We also show samples from models trained on the higher resolution images of the LSUN scene dataset.

The second article:

UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL
GENERATIVE ADVERSARIAL NETWORKS
Alec Radford & Luke Metz
indico Research
Boston, MA
falec,lukeg@indico.io
Soumith Chintala
Facebook AI Research

  • In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
  • Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning.
  • We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning.
  • Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator.
  • Additionally, we use the learned features for novel tasks – demonstrating their applicability as general image representations.

So again the QuEST interest here is Imagine we use the generative model and use the data not just the weights to generate the data – that is imagine that our previous idea of a conscious system that is separate from the subconscious system is wrong – imagine one system – but with processes that populate the sensory BU paths being what we call conscious and subconscious –

Imagine that even as early as the visual cortex that much of the content is inferred and not measured by the visual sensing (eyes) – this seems to me to be testable – by electrode studies confirm/refute the idea that much of what is present even early in the visual chain of processing is inferred versus captured by the eyes – this could account for the 10:1 feedback versus feedforward connections –

Here is the implication – we take Bernard’s generative models – and have them generate additional information (competing with the bottom up sensory data for populating the agent’s world model) – and then the winning populated solution gets processed by a bottom up deep learning experienced based solution –

 

Note ‘blending’ is now only the competition of the top down imagined information and the bottom up sensory data – but the cognition is all in the bottom up processing of the resulting world model

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