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Found 10 papers, 5 papers with code
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
Developing inherently interpretable models for prediction has gained prominence in recent years.
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 for Calibration
In this work, we argue that the likelihood of manifold intrusion increases with the distance between data to mix.
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.
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks.
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.
