Add to ORKGIn many machine learning tasks, the available training data has a skewed distribution- a small set of training classes for which a large number of examples are available (“base classes”), and many classes for which only a limited number of examples are available (fewshot classes). This is known as the long-tail distribution problem. Few-shot learning refers to understanding new concepts from only a few examples. Training a classifier on these fewexample classes is known as the few-shot classification task. Techniques disclosed herein improve classification accuracy for few-shot classes by leveraging examples from the base classes. A generative machine-learning model is trained using the base class examples and learns essential properties of...
Few-shot segmentation has in recent years gotten a lot of attention. The reason is its ability to se...
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot c...
Few-shot learning aims to train models that can be generalized to novel classes with only a few samp...
Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A su...
The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is ...
Deep learning has recently driven remarkable progress in several applications, including image class...
Few-shot classification requires deep neural networks to learn generalized representations only from...
A few-shot generative model should be able to generate data from a novel distribution by only observ...
We address the problem of few-shot classification where the goal is to learn a classifier from a lim...
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing ...
Few-shot learning addresses the challenge of learning how to address novel tasks given not just limi...
One of the fundamental problems in machine learning is training high-quality neural network models u...
Deep learning has achieved enormous success in various computer tasks. The excellent performance dep...
In recent years, there has been rapid progress in computing performance and communication techniques...
Doctor of PhilosophyDepartment of Computer ScienceWilliam H HsuHumans are capable of learning a spec...
Few-shot segmentation has in recent years gotten a lot of attention. The reason is its ability to se...
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot c...
Few-shot learning aims to train models that can be generalized to novel classes with only a few samp...
Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A su...
The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is ...
Deep learning has recently driven remarkable progress in several applications, including image class...
Few-shot classification requires deep neural networks to learn generalized representations only from...
A few-shot generative model should be able to generate data from a novel distribution by only observ...
We address the problem of few-shot classification where the goal is to learn a classifier from a lim...
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing ...
Few-shot learning addresses the challenge of learning how to address novel tasks given not just limi...
One of the fundamental problems in machine learning is training high-quality neural network models u...
Deep learning has achieved enormous success in various computer tasks. The excellent performance dep...
In recent years, there has been rapid progress in computing performance and communication techniques...
Doctor of PhilosophyDepartment of Computer ScienceWilliam H HsuHumans are capable of learning a spec...
Few-shot segmentation has in recent years gotten a lot of attention. The reason is its ability to se...
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot c...
Few-shot learning aims to train models that can be generalized to novel classes with only a few samp...
