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Found 9 papers, 4 papers with code
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks
In this thesis, we develop theoretical, algorithmic and experimental contributions for Machine Learning with limited labels, and more specifically for the tasks of Image Classification and Object Detection in Computer Vision.
Tailoring Mixup to Data using Kernel Warping functions
Data augmentation is an essential building block for learning efficient deep learning models.
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives.
Proposal-Contrastive Pretraining for Object Detection from Fewer Data
However, for unsupervised pretraining, the popular contrastive learning requires a large batch size and, therefore, a lot of resources.
Understanding deep neural networks through the lens of their non-linearity
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Optimal Transport as a Defense Against Adversarial Attacks
This leads to SAT (Sinkhorn Adversarial Training), a more robust defense against adversarial attacks.
Improving Few-Shot Learning through Multi-task Representation Learning Theory
In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task.
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms
To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of meta-learning theory into practice for the popular task of few-shot classification.
