2 关于 Few-shot Learning Techniques 从下往上分别是基于优化的方法、基于度量的方法、基于度量增强的方法: “Optimization-based”基于优化的方法:对网络采用优化的过程进行更改。 “Metric-based”基于度量的方法:获得一组样本之间的相似性得分。 “Augmented Metric-based“基于度量增强的方法:对基于度量的小样本学习...
近期看了一篇few-shot learning的综述论文,论文题目是《Generalizing from a Few Examples: A Survey on Few-Shot Learning》(https://arxiv.org/pdf/1904.05046.pdf),这篇论文已经被ACM Computing Surveys接收,作者还建立了 GitHub repo(https://github.com/tata1661/FewShotPapers),用于更新该领域的发展。 这篇...
EDA: Easy data augmentation techniques for boosting performance on textclassification tasks, in EMNLP ...
Some meta learning approaches work on a more abstract level, by training models to be easy to train. In traditional supervised learning, a model’s parameters (like weights and biases) are what’s “learned,” while the model’shyperparameters—like the learning rate, or how parameters are i...
This review article about Few-Shot Learning techniques is focused on Computer Vision Applications based on Deep Convolutional Neural Networks. A general discussion about Few-Shot Learning is given, featuring a context-constrained description, a short list of applications, a description of a couple of...
thousands of different tasks and domains, as well as for compliant reasons while dealing with sensitive user data. In this project, we develop techniques for few-shot and zero-shot learning to obtain state-of-the-art performance with Multilingual pre-TRainEd ModEls (XTREME) while using very few...
Learning for rare cases: By using few-shot learning, machines can learn rare cases. For example, when classifying images of animals, a machine learning model trained with few-shot learning techniques can classify an image of a rare species correctly after being exposed to small amount of prior...
Few-Shot Learning refers to the practice of feeding a machine learning model with a very small amount of training data to guide its predictions, like a few examples at inference time, as opposed to standard fine-tuning techniques which require a relatively large amount of training data for the...
Parameter-level FSL approaches involve the use of meta-learning that control the exploitation of models’ parameters to intelligently infer which features are important for the task at hand. FSL methods that constrain the parameter space and use regularization techniques fall under the category of ...
解决这个问题的一个有希望的方法就是few-shot classification is the family of metric learning techniques. 在这个系列中,标准的参数化线性分类头被一个与类无关的距离函数取代。class membership由潜在空间中一个或多个已知属于每一类的点的距离决定。简单的距离函数如余弦函数和欧几里得函数导致了令人惊讶的强大分类...