32 citations · 46 across the 5 of their papers we have counts for
12 papers
EvolGAN: Evolutionary Generative Adversarial Networks
Baptiste Roziere, Fabien Teytaud, Vlad Hosu +4
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new…
Tarsier: Evolving Noise Injection in Super-Resolution GANs
Baptiste Roziere, Nathanal Carraz Rakotonirina, Vlad Hosu +4
Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, w…
Critical analysis on the reproducibility of visual quality assessment using deep features
Franz Götz-Hahn, Vlad Hosu, Dietmar Saupe
Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets. This paper illustrates that complex data leakage case…
Comment on "No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features"
Franz Götz-Hahn, Vlad Hosu, Dietmar Saupe
In Neural Processing Letters 50,3 (2019) a machine learning approach to blind video quality assessment was proposed. It is based on temporal pooling of features of video frames, ta…
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning
Hanhe Lin, Vlad Hosu, Dietmar Saupe
Multi-level deep-features have been driving state-of-the-art methods for aesthetics and image quality assessment (IQA). However, most IQA benchmarks are comprised of artificially d…
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Hui Men, Vlad Hosu, Hanhe Lin +2
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predi…