Add to ORKGpeer reviewedAlthough supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has diminished significantly in recent years, indicating that a plateau is in sight. Meanwhile, the use of self-supervised learning (SSL) for the purpose of natural language processing (NLP) has seen tremendous successes during the past couple of years, with this new learning paradigm yielding powerful language models. Inspired by the excellent results obtained in the field of NLP, self-supervised methods that rely on clustering, contrastive learning, distillation, and information-maximization, which all fall under the banner of discriminative SSL, have exp...
The recent rise in machine learning has been largely made possible by novel algorithms, such as con...
With an ever-increasing amount of available image data, self-supervised learning (SSL) circumvents t...
In general, large-scale annotated data are essential to training deep neural networks in order to ac...
Although deep learning algorithms have achieved significant progress in a variety of domains, they r...
Transfer learning technique enables training Deep Learning (DL) models in a data-efficient way for s...
Owing to the existence of large labeled datasets, Deep Convolutional Neural Networks have ushered in...
Recent advancements in self-supervised learning (SSL) made it possible to learn generalizable visual...
Self-supervised learning (SSL) aims at extracting from abundant unlabelled images transferable seman...
Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and...
Recent advances in self-supervised learning (SSL) using large models to learn visual representations...
Self-supervised representation learning methods aim to provide powerful deep feature learning withou...
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remai...
The scarcity of high-quality annotated medical imaging datasets is a major problem that collides wit...
Recent self-supervised learning (SSL) methods have shown impressive results in learning visual repre...
This dissertation addresses three limitations of deep learning methods in image and video understand...
The recent rise in machine learning has been largely made possible by novel algorithms, such as con...
With an ever-increasing amount of available image data, self-supervised learning (SSL) circumvents t...
In general, large-scale annotated data are essential to training deep neural networks in order to ac...
Although deep learning algorithms have achieved significant progress in a variety of domains, they r...
Transfer learning technique enables training Deep Learning (DL) models in a data-efficient way for s...
Owing to the existence of large labeled datasets, Deep Convolutional Neural Networks have ushered in...
Recent advancements in self-supervised learning (SSL) made it possible to learn generalizable visual...
Self-supervised learning (SSL) aims at extracting from abundant unlabelled images transferable seman...
Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and...
Recent advances in self-supervised learning (SSL) using large models to learn visual representations...
Self-supervised representation learning methods aim to provide powerful deep feature learning withou...
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remai...
The scarcity of high-quality annotated medical imaging datasets is a major problem that collides wit...
Recent self-supervised learning (SSL) methods have shown impressive results in learning visual repre...
This dissertation addresses three limitations of deep learning methods in image and video understand...
The recent rise in machine learning has been largely made possible by novel algorithms, such as con...
With an ever-increasing amount of available image data, self-supervised learning (SSL) circumvents t...
In general, large-scale annotated data are essential to training deep neural networks in order to ac...