2 citations · 3 across the 4 of their papers we have counts for
5 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…
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…