3,605 research outputs found
The Perception-Distortion Tradeoff
Image restoration algorithms are typically evaluated by some distortion
measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify
perceived perceptual quality. In this paper, we prove mathematically that
distortion and perceptual quality are at odds with each other. Specifically, we
study the optimal probability for correctly discriminating the outputs of an
image restoration algorithm from real images. We show that as the mean
distortion decreases, this probability must increase (indicating worse
perceptual quality). As opposed to the common belief, this result holds true
for any distortion measure, and is not only a problem of the PSNR or SSIM
criteria. We also show that generative-adversarial-nets (GANs) provide a
principled way to approach the perception-distortion bound. This constitutes
theoretical support to their observed success in low-level vision tasks. Based
on our analysis, we propose a new methodology for evaluating image restoration
methods, and use it to perform an extensive comparison between recent
super-resolution algorithms.Comment: CVPR 2018 (long oral presentation), see talk at:
https://youtu.be/_aXbGqdEkjk?t=39m43
Institutional Cognition
We generalize a recent mathematical analysis of Bernard Baars' model of human consciousness to explore analogous, but far more complicated, phenomena of institutional cognition. Individual consciousness is limited to a single, tunable, giant component of interacting cogntivie modules, instantiating a Global Workspace. Human institutions, by contrast, seem able to multitask, supporting several such giant components simultaneously, although their behavior remains constrained to a topology generated by cultural context and by the path-dependence inherent to organizational history. Surprisingly, such multitasking, while clearly limiting the phenomenon of inattentional blindness, does not eliminate it. This suggests that organizations (or machines) explicitly designed along these principles, while highly efficient at certain sets of tasks, would still be subject to analogs of the subtle failure patterns explored in Wallace (2005b, 2006). We compare and contrast our results with recent work on collective efficacy and collective consciousness
Institutional paraconsciousness and its pathologies
This analysis extends a recent mathematical treatment of the Baars consciousness model to analogous, but far more complicated, phenomena of institutional cognition. Individual consciousness is limited to a single, tunable, giant component of interacting cognitive modules, instantiating a Global Workspace. Human institutions, by contrast, support several, sometimes many, such giant components simultaneously, although their behavior remains constrained to a topology generated by cultural context and by the path-dependence inherent to organizational history. Such highly parallel multitasking - institutional paraconsciousness - while clearly limiting inattentional blindness and the consequences of failures within individual workspaces, does not eliminate them, and introduces new characteristic dysfunctions involving the distortion of information sent between global workspaces. Consequently, organizations (or machines designed along these principles), while highly efficient at certain kinds of tasks, remain subject to canonical and idiosyncratic failure patterns similar to, but more complicated than, those afflicting individuals. Remediation is complicated by the manner in which pathogenic externalities can write images of themselves on both institutional function and therapeutic intervention, in the context of relentless market selection pressures. The approach is broadly consonant with recent work on collective efficacy, collective consciousness, and distributed cognition
On the Computation of the Gaussian Rate-Distortion-Perception Function
In this paper, we study the computation of the rate-distortion-perception
function (RDPF) for a multivariate Gaussian source under mean squared error
(MSE) distortion and, respectively, Kullback-Leibler divergence, geometric
Jensen-Shannon divergence, squared Hellinger distance, and squared
Wasserstein-2 distance perception metrics. To this end, we first characterize
the analytical bounds of the scalar Gaussian RDPF for the aforementioned
divergence functions, also providing the RDPF-achieving forward "test-channel"
realization. Focusing on the multivariate case, we establish that, for
tensorizable distortion and perception metrics, the optimal solution resides on
the vector space spanned by the eigenvector of the source covariance matrix.
Consequently, the multivariate optimization problem can be expressed as a
function of the scalar Gaussian RDPFs of the source marginals, constrained by
global distortion and perception levels. Leveraging this characterization, we
design an alternating minimization scheme based on the block nonlinear
Gauss-Seidel method, which optimally solves the problem while identifying the
Gaussian RDPF-achieving realization. Furthermore, the associated algorithmic
embodiment is provided, as well as the convergence and the rate of convergence
characterization. Lastly, for the "perfect realism" regime, the analytical
solution for the multivariate Gaussian RDPF is obtained. We corroborate our
results with numerical simulations and draw connections to existing results.Comment: This paper has been submitted for journal publicatio
Machine Hyperconsciousness
Individual animal consciousness appears limited to a single giant component of interacting cognitive modules, instantiating a shifting, highly tunable, Global Workspace. Human institutions, by contrast, can support several, often many, such giant components simultaneously, although they generally function far more slowly than the minds of the individuals who compose them. Machines having multiple global workspaces -- hyperconscious machines -- should, however, be able to operate at the few hundred milliseconds characteistic of individual consciousness. Such multitasking -- machine or institutional -- while clearly limiting the phenomenon of inattentional blindness, does not eliminate it, and introduces characteristic failure modes involving the distortion of information sent between global workspaces. This suggests that machines explicitly designed along these principles, while highly efficient at certain sets of tasks, remain subject to canonical and idiosyncratic failure patterns analogous to, but more complicated than, those explored in Wallace (2006a). By contrast, institutions, facing similar challenges, are usually deeply embedded in a highly stabilizing cultural matrix of law, custom, and tradition which has evolved over many centuries. Parallel development of analogous engineering strategies, directed toward ensuring an 'ethical' device, would seem requisite to the sucessful application of any form of hyperconscious machine technology
Adaptive Semantic Communications: Overfitting the Source and Channel for Profit
Most semantic communication systems leverage deep learning models to provide
end-to-end transmission performance surpassing the established source and
channel coding approaches. While, so far, research has mainly focused on
architecture and model improvements, but such a model trained over a full
dataset and ergodic channel responses is unlikely to be optimal for every test
instance. Due to limitations on the model capacity and imperfect optimization
and generalization, such learned models will be suboptimal especially when the
testing data distribution or channel response is different from that in the
training phase, as is likely to be the case in practice. To tackle this, in
this paper, we propose a novel semantic communication paradigm by leveraging
the deep learning model's overfitting property. Our model can for instance be
updated after deployment, which can further lead to substantial gains in terms
of the transmission rate-distortion (RD) performance. This new system is named
adaptive semantic communication (ASC). In our ASC system, the ingredients of
wireless transmitted stream include both the semantic representations of source
data and the adapted decoder model parameters. Specifically, we take the
overfitting concept to the extreme, proposing a series of ingenious methods to
adapt the semantic codec or representations to an individual data or channel
state instance. The whole ASC system design is formulated as an optimization
problem whose goal is to minimize the loss function that is a tripartite
tradeoff among the data rate, model rate, and distortion terms. The experiments
(including user study) verify the effectiveness and efficiency of our ASC
system. Notably, the substantial gain of our overfitted coding paradigm can
catalyze semantic communication upgrading to a new era
Biologically inspired distributed machine cognition: a new formal approach to hyperparallel computation
The irresistable march toward multiple-core chip technology presents currently intractable pdrogramming challenges. High level mental processes in many animals, and their analogs for social structures, appear similarly massively parallel, and recent mathematical models addressing them may be adaptable to the multi-core programming problem
- …