3 citations · 9 across the 6 of their papers we have counts for
7 papers
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units
Sara Meftah, Nasredine Semmar, Youssef Tamaazousti +2
Neural Transfer Learning (TL) is becoming ubiquitous in Natural Language Processing (NLP), thanks to its high performance on many tasks, especially in low-resourced scenarios. Nota…
Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting
Ahmed Ben Saad, Youssef Tamaazousti, Josselin Kherroubi +1
We tackle the problem of texture inpainting where the input images are textures with missing values along with masks that indicate the zones that should be generated. Many works ha…
How to make a pizza: Learning a compositional layer-based GAN model
Dim P. Papadopoulos, Youssef Tamaazousti, Ferda Ofli +2
A food recipe is an ordered set of instructions for preparing a particular dish. From a visual perspective, every instruction step can be seen as a way to change the visual appeara…
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Sara Meftah, Youssef Tamaazousti, Nasredine Semmar +2
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems a…
Deep Multi-class Adversarial Specularity Removal
John Lin, Mohamed El Amine Seddik, Mohamed Tamaazousti +2
We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single imag…
Learning Finer-class Networks for Universal Representations
Julien Girard, Youssef Tamaazousti, Hervé Le Borgne +1
Many real-world visual recognition use-cases can not directly benefit from state-of-the-art CNN-based approaches because of the lack of many annotated data. The usual approach to d…
Learning More Universal Representations for Transfer-Learning
Youssef Tamaazousti, Hervé Le Borgne, Céline Hudelot +2
A representation is supposed universal if it encodes any element of the visual world (e.g., objects, scenes) in any configuration (e.g., scale, context). While not expecting pure u…