Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

While several research studies have focused on analyzing human behavior and, in particular, emotional signals from visual data, the problem of synthesizing face video sequences with specific attributes (e.g. age, facial expressions) received much less atte ...

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missin ...

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...

Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

Generating melody from lyrics to compose a song has been a very interesting research topic in the area of artificial intelligence and music, which tries to predict generative music relationship between lyrics and melody. In this demonstration paper, by exp ...

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

Existing approaches to distribute Generative Adversarial Networks (GANs) either (i) fail to scale for they typically put the two components of a GAN (the generator and the discriminator) on different machines, inducing significant communication overhead, o ...

Energy-based models (EBMs) are experiencing a resurgence of interest in both the physics community and the machine learning community. This article provides an intuitive introduction to EBMs, without requiring any background in machine learning, connecting ...

In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C2GAN) for the task of keypoint-guided image generation. The proposed C2GAN is a cross-modal framework exploring a joint exploitation of the keypoint and the image data in an i ...

Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. ...

In past years, we have witnessed the fields of geosciences and remote sensing and artificial intelligence (AI) become closer. Thanks to the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have f ...

State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every ...

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum- ...

Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...

Melody generation from lyrics has been a challenging research issue in the field of artificial intelligence and music, which enables us to learn and discover latent relationships between interesting lyrics and accompanying melodies. Unfortunately, the limi ...

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Chat with Graph Search

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

DeepNC: Deep Generative Network Completion

A Structured Dictionary Perspective on Implicit Neural Representations

Generative adversarial training of product of policies for robust and adaptive movement primitives

Deep Generative Models and Applications

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Lyrics-Conditioned Neural Melody Generation

Multi-layer State Evolution Under Random Convolutional Design

FeGAN: Scaling Distributed GANs

The physics of energy-based models

Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

Toward a Collective Agenda on AI for Earth Science Data Analysis

Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Context-Aware Learning for Generative Models

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Conditional LSTM-GAN for Melody Generation from Lyrics