Add to ORKGFew-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: support sets, no matter how small, can still include mislabeled samples. Robustness to label noise is therefore essential for FSL methods to be practical, but this problem surprisingly remains largely unexplored. To address mislabeled samples in FSL settings, we make several technical contributions. (1) We offer simple, yet effective, feature aggregation methods, improving the prototypes used by ProtoNet, a popular FSL technique. (2) We describe a novel Transformer model for Noisy Few-Shot Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to weigh ...
Despite the large progress in supervised learning with neural networks, there are significant challe...
Few sample learning (FSL) is significant and challenging in the field of machine learning. The capab...
Few-shot learning (FSL) aims to recognize target classes by adapting the prior knowledge learned fro...
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot c...
Few-shot classification aims at recognising novel categories with very limited labelled samples. Alt...
In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from out...
Learning with noisy labels is a vital topic for practical deep learning as models should be robust t...
Few-shot learning amounts to learning representations and acquiring knowledge such that novel tasks ...
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing ...
Despite impressive progress in deep learning, generalizing far beyond the training distribution is a...
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which preven...
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the ...
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small...
The high cost of acquiring and annotating samples has made the `few-shot' learning problem of prime ...
We address the problem of few-shot classification where the goal is to learn a classifier from a lim...
Despite the large progress in supervised learning with neural networks, there are significant challe...
Few sample learning (FSL) is significant and challenging in the field of machine learning. The capab...
Few-shot learning (FSL) aims to recognize target classes by adapting the prior knowledge learned fro...
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot c...
Few-shot classification aims at recognising novel categories with very limited labelled samples. Alt...
In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from out...
Learning with noisy labels is a vital topic for practical deep learning as models should be robust t...
Few-shot learning amounts to learning representations and acquiring knowledge such that novel tasks ...
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing ...
Despite impressive progress in deep learning, generalizing far beyond the training distribution is a...
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which preven...
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the ...
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small...
The high cost of acquiring and annotating samples has made the `few-shot' learning problem of prime ...
We address the problem of few-shot classification where the goal is to learn a classifier from a lim...
Despite the large progress in supervised learning with neural networks, there are significant challe...
Few sample learning (FSL) is significant and challenging in the field of machine learning. The capab...
Few-shot learning (FSL) aims to recognize target classes by adapting the prior knowledge learned fro...