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Found 17 papers, 9 papers with code
Texture Synthesis with Spatial Generative Adversarial Networks
Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data.
Learning Texture Manifolds with the Periodic Spatial GAN
Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset.
The Conditional Analogy GAN: Swapping Fashion Articles on People Images
We present a novel method to solve image analogy problems : it allows to learn the relation between paired images present in training data, and then generalize and generate images that correspond to the relation, but were never seen in the training set.
GANosaic: Mosaic Creation with Generative Texture Manifolds
This paper presents a novel framework for generating texture mosaics with convolutional neural networks.
Stochastic Maximum Likelihood Optimization via Hypernetworks
This work explores maximum likelihood optimization of neural networks through hypernetworks.
First Order Generative Adversarial Networks
To formally describe an optimal update direction, we introduce a theoretical framework which allows the derivation of requirements on both the divergence and corresponding method for determining an update direction, with these requirements guaranteeing unbiased mini-batch updates in the direction of steepest descent.
Ranked #2 on
Image Generation
on LSUN Bedroom 64 x 64
Disentangling Multiple Conditional Inputs in GANs
In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs).
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization.
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward.
Unlabeled Disentangling of GANs with Guided Siamese Networks
In addition, we illustrate that simple guidance functions we use in UD-GAN-G allow us to directly capture the desired variations in the data.
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.
A Hierarchical Bayesian Model for Size Recommendation in Fashion
We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion.
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Visualizing an outfit is an essential part of shopping for clothes.
Conditional Image Generation
Vocal Bursts Intensity Prediction
Set Flow: A Permutation Invariant Normalizing Flow
We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data.
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages
Cutting and pasting image segments feels intuitive: the choice of source templates gives artists flexibility in recombining existing source material.
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.
