136 research outputs found
Improving GAN with neighbors embedding and gradient matching
We propose two new techniques for training Generative Adversarial Networks
(GANs). Our objectives are to alleviate mode collapse in GAN and improve the
quality of the generated samples. First, we propose neighbor embedding, a
manifold learning-based regularization to explicitly retain local structures of
latent samples in the generated samples. This prevents generator from producing
nearly identical data samples from different latent samples, and reduces mode
collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we
propose a new technique, gradient matching, to align the distributions of the
generated samples and the real samples. As it is challenging to work with
high-dimensional sample distributions, we propose to align these distributions
through the scalar discriminator scores. We constrain the difference between
the discriminator scores of the real samples and generated ones. We further
constrain the difference between the gradients of these discriminator scores.
We derive these constraints from Taylor approximations of the discriminator
function. We perform experiments to demonstrate that our proposed techniques
are computationally simple and easy to be incorporated in existing systems.
When Gradient matching and Neighbour embedding are applied together, our GN-GAN
achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets,
e.g. FID score of for the STL-10 dataset. Our code is available at:
https://github.com/tntrung/ganComment: Published as a conference paper at AAAI 201
Graph Priors, Optimal Transport, and Deep Learning in Biomedical Discovery
Recent advances in biomedical data collection allows the collection of massive datasets measuring thousands of features in thousands to millions of individual cells. This data has the potential to advance our understanding of biological mechanisms at a previously impossible resolution. However, there are few methods to understand data of this scale and type. While neural networks have made tremendous progress on supervised learning problems, there is still much work to be done in making them useful for discovery in data with more difficult to represent supervision. The flexibility and expressiveness of neural networks is sometimes a hindrance in these less supervised domains, as is the case when extracting knowledge from biomedical data. One type of prior knowledge that is more common in biological data comes in the form of geometric constraints. In this thesis, we aim to leverage this geometric knowledge to create scalable and interpretable models to understand this data. Encoding geometric priors into neural network and graph models allows us to characterize the models’ solutions as they relate to the fields of graph signal processing and optimal transport. These links allow us to understand and interpret this datatype. We divide this work into three sections. The first borrows concepts from graph signal processing to construct more interpretable and performant neural networks by constraining and structuring the architecture. The second borrows from the theory of optimal transport to perform anomaly detection and trajectory inference efficiently and with theoretical guarantees. The third examines how to compare distributions over an underlying manifold, which can be used to understand how different perturbations or conditions relate. For this we design an efficient approximation of optimal transport based on diffusion over a joint cell graph. Together, these works utilize our prior understanding of the data geometry to create more useful models of the data. We apply these methods to molecular graphs, images, single-cell sequencing, and health record data
Representation learning in unsupervised domain translation
Ce mémoire s'adresse au problème de traduction de domaine non-supervisée. La traduction non-supervisée cherche à traduire un domaine, le domaine source, à un domaine cible sans supervision. Nous étudions d'abord le problème en utilisant le formalisme du transport optimal. Dans un second temps, nous étudions le problème de transfert de sémantique à haut niveau dans les images en utilisant les avancés en apprentissage de représentations et de transfert d'apprentissages développés dans la communauté d'apprentissage profond.
Le premier chapitre est dévoué à couvrir les bases des concepts utilisés dans ce travail. Nous décrivons d'abord l'apprentissage de représentation en incluant la description de réseaux de neurones et de l'apprentissage supervisé et non supervisé. Ensuite, nous introduisons les modèles génératifs et le transport optimal. Nous terminons avec des notions pertinentes sur le transfert d'apprentissages qui seront utiles pour le chapitre 3.
Le deuxième chapitre présente \textit{Neural Wasserstein Flow}. Dans ce travail, nous construisons sur la théorie du transport optimal et démontrons que les réseaux de neurones peuvent être utilisés pour apprendre des barycentres de Wasserstein. De plus, nous montrons que les réseaux de neurones peuvent amortir n'importe quel barycentre, permettant d'apprendre une interpolation continue. Nous montrons aussi comment utiliser ces concepts dans le cadre des modèles génératifs. Finalement, nous montrons que notre approche permet d'interpoler des formes et des couleurs.
Dans le troisième chapitre, nous nous attaquons au problème de transfert de sémantique haut niveau dans les images. Nous montrons que ceci peut être obtenu simplement avec un GAN conditionné sur la représentation apprise par un réseau de neurone. Nous montrons aussi comment ce processus peut être rendu non-supervisé si la représentation apprise est un regroupement. Finalement, nous montrons que notre approche fonctionne sur la tâche de transfert de MNIST à SVHN.
Nous concluons en mettant en relation les deux contributions et proposons des travaux futures dans cette direction.This thesis is concerned with the problem of unsupervised domain translation. Unsupervised domain translation is the task of transferring one domain, the source domain, to a target domain. We first study this problem using the formalism of optimal transport. Next, we study the problem of high-level semantic image to image translation using advances in representation learning and transfer learning.
The first chapter is devoted to reviewing the background concepts used in this work. We first describe representation learning including a description of neural networks and supervised and unsupervised representation learning. We then introduce generative models and optimal transport. We finish with the relevant notions of transfer learning that will be used in chapter 3.
The second chapter presents Neural Wasserstein Flow. In this work, we build on the theory of optimal transport and show that deep neural networks can be used to learn a Wasserstein barycenter of distributions. We further show how a neural network can amortize any barycenter yielding a continuous interpolation. We also show how this idea can be used in the generative model framework. Finally, we show results on shape interpolation and colour interpolation.
In the third chapter, we tackle the task of high level semantic image to image translation. We show that high level semantic image to image translation can be achieved by simply learning a conditional GAN with the representation learned from a neural network. We further show that we can make this process unsupervised if the representation learning is a clustering. Finally, we show that our approach works on the task of MNIST to SVHN
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Generative adversarial networks (GANs) have been extensively studied in the
past few years. Arguably their most significant impact has been in the area of
computer vision where great advances have been made in challenges such as
plausible image generation, image-to-image translation, facial attribute
manipulation and similar domains. Despite the significant successes achieved to
date, applying GANs to real-world problems still poses significant challenges,
three of which we focus on here. These are: (1) the generation of high quality
images, (2) diversity of image generation, and (3) stable training. Focusing on
the degree to which popular GAN technologies have made progress against these
challenges, we provide a detailed review of the state of the art in GAN-related
research in the published scientific literature. We further structure this
review through a convenient taxonomy we have adopted based on variations in GAN
architectures and loss functions. While several reviews for GANs have been
presented to date, none have considered the status of this field based on their
progress towards addressing practical challenges relevant to computer vision.
Accordingly, we review and critically discuss the most popular
architecture-variant, and loss-variant GANs, for tackling these challenges. Our
objective is to provide an overview as well as a critical analysis of the
status of GAN research in terms of relevant progress towards important computer
vision application requirements. As we do this we also discuss the most
compelling applications in computer vision in which GANs have demonstrated
considerable success along with some suggestions for future research
directions. Code related to GAN-variants studied in this work is summarized on
https://github.com/sheqi/GAN_Review.Comment: Accepted by ACM Computing Surveys, 23 November 202
Quantum Earth Mover's Distance: A New Approach to Learning Quantum Data
Quantifying how far the output of a learning algorithm is from its target is
an essential task in machine learning. However, in quantum settings, the loss
landscapes of commonly used distance metrics often produce undesirable outcomes
such as poor local minima and exponentially decaying gradients. As a new
approach, we consider here the quantum earth mover's (EM) or Wasserstein-1
distance, recently proposed in [De Palma et al., arXiv:2009.04469] as a quantum
analog to the classical EM distance. We show that the quantum EM distance
possesses unique properties, not found in other commonly used quantum distance
metrics, that make quantum learning more stable and efficient. We propose a
quantum Wasserstein generative adversarial network (qWGAN) which takes
advantage of the quantum EM distance and provides an efficient means of
performing learning on quantum data. Our qWGAN requires resources polynomial in
the number of qubits, and our numerical experiments demonstrate that it is
capable of learning a diverse set of quantum data
Two-sample Test with Kernel Projected Wasserstein Distance
We develop a kernel projected Wasserstein distance for the two-sample test,
an essential building block in statistics and machine learning: given two sets
of samples, to determine whether they are from the same distribution. This
method operates by finding the nonlinear mapping in the data space which
maximizes the distance between projected distributions. In contrast to existing
works about projected Wasserstein distance, the proposed method circumvents the
curse of dimensionality more efficiently. We present practical algorithms for
computing this distance function together with the non-asymptotic uncertainty
quantification of empirical estimates. Numerical examples validate our
theoretical results and demonstrate good performance of the proposed method.Comment: 49 pages, 10 figures, 4 table
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