Few-shot learning-based human activity recognition
نویسندگان
چکیده
منابع مشابه
Few-shot Learning
Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a classifier has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity classifiers requires many iterative steps over many examples to perform well. Here, we propose ...
متن کاملOne-shot and few-shot learning of word embeddings
Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us. ...
متن کاملFew-Shot Learning with Graph Neural Networks
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recentl...
متن کاملFew-shot Classification by Learning Disentangled Representations
Machine learning has improved state-of-the art performance in numerous domains, by using large amounts of data. In reality, labelled data is often not available for the task of interest. A fundamental problem of artificial intelligence is finding a representation that can generalize to never seen before classes. In this research, the power of generative models is combined with disentangled repr...
متن کاملPrototypical Networks for Few-shot Learning
A recent approach to few-shot classification called matching networks has demonstrated the benefits of coupling metric learning with a training procedure that mimics test. This approach relies on an attention scheme that forms a distribution over all points in the support set, scaling poorly with its size. We propose a more streamlined approach, prototypical networks, that learns a metric space...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Expert Systems with Applications
سال: 2019
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2019.06.070