7 citations · 10 across the 4 of their papers we have counts for
5 papers
Cost Savings from Automatic Quality Assessment of Generated Images
Xavier Giro-i-Nieto, Nefeli Andreou, Anqi Liang +3
Deep generative models have shown impressive progress in recent years, making it possible to produce high quality images with a simple text prompt or a reference image. However, st…
Feedback-Driven Vision-Language Alignment with Minimal Human Supervision
Giorgio Giannone, Ruoteng Li, Qianli Feng +3
Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for ex…
Structural Similarity: When to Use Deep Generative Models on Imbalanced Image Dataset Augmentation
Chenqi Guo, Fabian Benitez-Quiroz, Qianli Feng +1
Improving the performance on an imbalanced training set is one of the main challenges in nowadays Machine Learning. One way to augment and thus re-balance the image dataset is thro…
Near Perfect GAN Inversion
Qianli Feng, Viraj Shah, Raghudeep Gadde +2
To edit a real photo using Generative Adversarial Networks (GANs), we need a GAN inversion algorithm to identify the latent vector that perfectly reproduces it. Unfortunately, wher…
When do GANs replicate? On the choice of dataset size
Qianli Feng, Chenqi Guo, Fabian Benitez-Quiroz +1
Do GANs replicate training images? Previous studies have shown that GANs do not seem to replicate training data without significant change in the training procedure. This leads to…