135 research outputs found
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth pursuing to produce more accurate and visually pleasing
reconstructions at much deeper compression levels for both image and video
data. We also demonstrate that generative compression is orders-of-magnitude
more resilient to bit error rates (e.g. from noisy wireless channels) than
traditional variable-length coding schemes
Mode Regularized Generative Adversarial Networks
Although Generative Adversarial Networks achieve state-of-the-art results on
a variety of generative tasks, they are regarded as highly unstable and prone
to miss modes. We argue that these bad behaviors of GANs are due to the very
particular functional shape of the trained discriminators in high dimensional
spaces, which can easily make training stuck or push probability mass in the
wrong direction, towards that of higher concentration than that of the data
generating distribution. We introduce several ways of regularizing the
objective, which can dramatically stabilize the training of GAN models. We also
show that our regularizers can help the fair distribution of probability mass
across the modes of the data generating distribution, during the early phases
of training and thus providing a unified solution to the missing modes problem.Comment: Published as a conference paper at ICLR 201
Neural-based Compression Scheme for Solar Image Data
Studying the solar system and especially the Sun relies on the data gathered
daily from space missions. These missions are data-intensive and compressing
this data to make them efficiently transferable to the ground station is a
twofold decision to make. Stronger compression methods, by distorting the data,
can increase data throughput at the cost of accuracy which could affect
scientific analysis of the data. On the other hand, preserving subtle details
in the compressed data requires a high amount of data to be transferred,
reducing the desired gains from compression. In this work, we propose a neural
network-based lossy compression method to be used in NASA's data-intensive
imagery missions. We chose NASA's SDO mission which transmits 1.4 terabytes of
data each day as a proof of concept for the proposed algorithm. In this work,
we propose an adversarially trained neural network, equipped with local and
non-local attention modules to capture both the local and global structure of
the image resulting in a better trade-off in rate-distortion (RD) compared to
conventional hand-engineered codecs. The RD variational autoencoder used in
this work is jointly trained with a channel-dependent entropy model as a shared
prior between the analysis and synthesis transforms to make the entropy coding
of the latent code more effective. Our neural image compression algorithm
outperforms currently-in-use and state-of-the-art codecs such as JPEG and
JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet
(EUV) data. As a proof of concept for use of this algorithm in SDO data
analysis, we have performed coronal hole (CH) detection using our compressed
images, and generated consistent segmentations, even at a compression rate of
bits per pixel (compared to 8 bits per pixel on the original data)
using EUV data from SDO.Comment: Accepted for publication in IEEE Transactions on Aerospace and
Electronic Systems (TAES). arXiv admin note: text overlap with
arXiv:2210.0647
Representation Learning for Visual Data
Cette thĂšse par article contribue au domaine de lâapprentissage de reprĂ©sentations profondes, et plus prĂ©cisĂ©ment celui des modĂšles gĂ©nĂ©ratifs profonds, par lâentremise de travaux sur les machines de Boltzmann restreintes, les modĂšles gĂ©nĂ©ratifs adversariels ainsi que le pastiche automatique.
Le premier article sâintĂ©resse au problĂšme de lâestimation du gradient de la phase nĂ©gative des machines de Boltzmann par lâĂ©chantillonnage dâune rĂ©alisation physique du modĂšle. Nous prĂ©sentons une Ă©valuation empirique de lâimpact sur la performance, mesurĂ©e par log-vraisemblance nĂ©gative, de diverses contraintes associĂ©es Ă lâimplĂ©mentation physique de machines de Boltzmann restreintes (RBMs), soit le bruit sur les paramĂštres, lâamplitude limitĂ©e des paramĂštres et une connectivitĂ© limitĂ©e.
Le second article sâattaque au problĂšme de lâinfĂ©rence dans les modĂšles gĂ©nĂ©ratifs adversariels (GANs). Nous proposons une extension du modĂšle appelĂ©e infĂ©rence adversativement apprise (ALI) qui a la particularitĂ© dâapprendre jointement lâinfĂ©rence et la gĂ©nĂ©ration Ă partir dâun principe adversariel. Nous montrons que la reprĂ©sentation apprise par le modĂšle est utile Ă la rĂ©solution de tĂąches auxiliaires comme lâapprentissage semi-supervisĂ© en obtenant une performance comparable Ă lâĂ©tat de lâart pour les ensembles de donnĂ©es SVHN et CIFAR10.
Finalement, le troisiĂšme article propose une approche simple et peu coĂ»teuse pour entraĂźner un rĂ©seau unique de pastiche automatique Ă imiter plusieurs styles artistiques. Nous prĂ©sentons un mĂ©canisme de conditionnement, appelĂ© normalisation conditionnelle par instance, qui permet au rĂ©seau dâimiter plusieurs styles en parallĂšle via lâapprentissage dâun ensemble de paramĂštres de normalisation unique Ă chaque style. Ce mĂ©canisme sâavĂšre trĂšs efficace en pratique et a inspirĂ© plusieurs travaux subsĂ©quents qui ont appliquĂ© lâidĂ©e Ă des problĂšmes au-delĂ du domaine du pastiche automatique.This thesis by articles contributes to the field of deep learning, and more specifically the subfield of deep generative modeling, through work on restricted Boltzmann machines, generative adversarial networks and style transfer networks.
The first article examines the idea of tackling the problem of estimating the negative phase gradients in Boltzmann machines by sampling from a physical implementation of the model. We provide an empirical evaluation of the impact of various constraints associated with physical implementations of restricted Boltzmann machines (RBMs), namely noisy parameters, finite parameter amplitude and restricted connectivity patterns, on their performance as measured by negative log-likelihood through software simulation.
The second article tackles the inference problem in generative adversarial networks (GANs). It proposes a simple and straightforward extension to the GAN framework, named adversarially learned inference (ALI), which allows inference to be learned jointly with generation in a fully-adversarial framework. We show that the learned representation is useful for auxiliary tasks such as semi-supervised learning by obtaining a performance competitive with the then-state-of-the-art on the SVHN and CIFAR10 semi-supervised learning tasks.
Finally, the third article proposes a simple and scalable technique to train a single feedforward style transfer network to model multiple styles. It introduces a conditioning mechanism named conditional instance normalization which allows the network to capture multiple styles in parallel by learning a different set of instance normalization parameters for each style. This mechanism is shown to be very efficient and effective in practice, and has inspired multiple efforts to adapt the idea to problems outside of the artistic style transfer domain
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