48 citations · 112 across the 8 of their papers we have counts for
12 papers
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework
Hannah Janmohamed, Joao Correia, Penousal Machado +1
Denoising Diffusion Probabilistic Models have emerged as powerful generative priors across domains ranging from molecular design to high-resolution image synthesis. However, standa…
Inference-Time Optimization of Prompt Embeddings in Diffusion Models: A Comparison of sep-CMA-ES and Adam
Domício Pereira Neto, João Correia, Penousal Machado
Deep diffusion models have revolutionized image generation by producing high-quality outputs. However, achieving specific objectives with these models often requires costly adaptat…
Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning
Adriano Vinhas, João Correia, Penousal Machado
Deep Neural Networks (DNNs) have been successfully applied to a wide range of problems. However, two main limitations are commonly pointed out. The first one is that they require l…
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution Strategies
Victor Costa, Nuno Lourenço, João Correia +1
In the context of generative models, text-to-image generation achieved impressive results in recent years. Models using different approaches were proposed and trained in huge datas…
Speed Benchmarking of Genetic Programming Frameworks
Francisco Baeta, João Correia, Tiago Martins +1
Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate thi…
TensorGP -- Genetic Programming Engine in TensorFlow
Francisco Baeta, João Correia, Tiago Martins +1
In this paper, we resort to the TensorFlow framework to investigate the benefits of applying data vectorization and fitness caching methods to domain evaluation in Genetic Programm…
Demonstrating the Evolution of GANs through t-SNE
Victor Costa, Nuno Lourenço, João Correia +1
Generative Adversarial Networks (GANs) are powerful generative models that achieved strong results, mainly in the image domain. However, the training of GANs is not trivial, presen…
Exploring the Evolution of GANs through Quality Diversity
Victor Costa, Nuno Lourenço, João Correia +1
Generative adversarial networks (GANs) achieved relevant advances in the field of generative algorithms, presenting high-quality results mainly in the context of images. However, G…
Using Skill Rating as Fitness on the Evolution of GANs
Victor Costa, Nuno Lourenço, João Correia +1
Generative Adversarial Networks (GANs) are an adversarial model that achieved impressive results on generative tasks. In spite of the relevant results, GANs present some challenges…
COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks
Victor Costa, Nuno Lourenço, João Correia +1
Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to…
Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
Filipe Assunção, João Correia, Rúben Conceição +4
The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propo…
Evotype: Towards the Evolution of Type Stencils
Tiago Martins, João Correia, Ernesto Costa +1
Typefaces are an essential resource employed by graphic designers. The increasing demand for innovative type design work increases the need for good technological means to assist t…