The mechanisms behind the success of multi-view self-supervised learning...
The generative modeling landscape has experienced tremendous growth in r...
Text-based game environments are challenging because agents must deal wi...
Continual Learning, also known as Lifelong or Incremental Learning, has
...
Synthetic image generation has recently experienced significant improvem...
Large pre-trained models have proved to be remarkable zero- and
(prompt-...
Handling out-of-distribution (OOD) samples has become a major stake in t...
A well-known failure mode of neural networks corresponds to high confide...
Standard gradient descent algorithms applied to sequences of tasks are k...
Text-based dialogues are now widely used to solve real-world problems. I...
Rapid development of large-scale pre-training has resulted in foundation...
Data augmentation is a widely employed technique to alleviate the proble...
In this paper, we explore the use of GAN-based few-shot data augmentatio...
Transfer learning from large-scale pre-trained models has become essenti...
Modularity is a compelling solution to continual learning (CL), the prob...
Labeling data is often expensive and time-consuming, especially for task...
Recent work has made significant progress in learning object meshes with...
The field of Continual Learning (CL) seeks to develop algorithms that
ac...
Dataset bias is one of the prevailing causes of unfairness in machine
le...
Remote sensing and automatic earth monitoring are key to solve global-sc...
Explainability for machine learning models has gained considerable atten...
Convolutional neural networks are the most successful models in single i...
Cattle farming is responsible for 8.8% of greenhouse gas emissions
world...
Aquaculture industries rely on the availability of accurate fish body
me...
Progress in the field of machine learning has been fueled by the introdu...
In the last few years, we have witnessed a renewed and fast-growing inte...
Super-resolution (SR) has achieved great success due to the development ...
Acquiring count annotations generally requires less human effort than
po...
Acquiring count annotations generally requires less human effort than
po...
Learning from non-stationary data remains a great challenge for machine
...
Few-shot classification is challenging because the data distribution of ...
We propose a novel attention mechanism to enhance Convolutional Neural
N...
Target encoding plays a central role when learning Convolutional Neural
...
Few-shot learning has become essential for producing models that general...
Social media, as a major platform for communication and information exch...
Regularization is key for deep learning since it allows training more co...