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Found 11 papers, 1 papers with code
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets
Transfer Learning has been shown to be a powerful tool for Natural Language Processing (NLP) and has outperformed the standard supervised learning paradigm, as it takes benefit from the pre-learned knowledge.
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units
In the standard fine-tuning scheme of TL, a model is initially pre-trained on a source domain and subsequently fine-tuned on a target domain and, therefore, source and target domains are trained using the same architecture.
Multi-Task Supervised Pretraining for Neural Domain Adaptation
Two prevalent transfer learning approaches are used in recent works to improve neural networks performance for domains with small amounts of annotated data: Multi-task learning which involves training the task of interest with related auxiliary tasks to exploit their underlying similarities, and Mono-task fine-tuning, where the weights of the model are initialized with the pretrained weights of a large-scale labeled source domain and then fine-tuned with labeled data of the target domain (domain of interest).
Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting
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.
Exploration de l'apprentissage par transfert pour l'analyse de textes des r\'eseaux sociaux (Exploring neural transfer learning for social media text analysis )
L{'}apprentissage par transfert repr{\'e}sente la capacit{\'e} qu{'}un mod{\`e}le neuronal entra{\^\i}n{\'e} sur une t{\^a}che {\`a} g{\'e}n{\'e}raliser suffisamment et correctement pour produire des r{\'e}sultats pertinents sur une autre t{\^a}che proche mais diff{\'e}rente.
How to make a pizza: Learning a compositional layer-based GAN model
From a visual perspective, every instruction step can be seen as a way to change the visual appearance of the dish by adding extra objects (e. g., adding an ingredient) or changing the appearance of the existing ones (e. g., cooking the dish).
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains.
Ranked #1 on
Part-Of-Speech Tagging
on Social media
Deep Multi-class Adversarial Specularity Removal
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 image by generating its diffuse component.
Learning Finer-class Networks for Universal Representations
This raises the question of how well the original representation is "universal", that is to say directly adapted to many different target-tasks.
Learning More Universal Representations for Transfer-Learning
We also propose a unified framework of the methods based on the diversifying of the training problem.
MuCaLe-Net: Multi Categorical-Level Networks to Generate More Discriminating Features
In a transfer-learning scheme, the intermediate layers of a pre-trained CNN are employed as universal image representation to tackle many visual classification problems.
