While humans are generally able to separate the noisy samples from the clean samples with some scrutiny, this is in large part due to prior conceptual understandings of the classes depicted. Few-shot models presented with support sets such as those in Fig. 1 are tasked with learning...
几篇论文实现代码:《Few-shot learning with noisy labels》(CVPR 2022) GitHub: github.com/facebookresearch/noisy_few_shot 《GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribut...
A Novel Transformer-based Few-Shot Learning Method for Intelligent Fault Diagnosis with Noisy Labels under Varying Working ConditionsKeywordsFew-shot learningnoisy labelintelligent fault diagnosistransformerasymmetric loss functionRecent years have witnessed the success of few-shot learning (FSL) methods in ...
When Source-Free Domain Adaptation Meets Learning with Noisy Labels ICLR 2023 University of Western Ontario arxiv.org/pdf/2301.1338 最近的无源域自适应(source-free domain adaptation, SFDA)方法专注于在特征空间中学习有意义的聚类结构,成功地在不访问私有源数据的情况下将源域知识自适应到无标记的目标域。然...
Few-shot learning models attempt to emulate this by learning a general understanding of the data's underlying structure from previous experiences. This knowledge is then leveraged to adapt quickly to new tasks with minimal additional information, just like the child recognizing a new dog breed based...
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding Subhabrata (Subho) Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Chris Meek, Ahmed Awadallah, Jianfeng Gao NeurIPS 2021 | December 2021 Publication Github Self-training with Few-shot Rational...
Such a pooling also per- formed well in few-shot learning [45]. Thus, we employ second-order pooling with Sigmoid. 3. Approach Our pipeline builds on the generic few-shot Relation Net pipeline [34] which learns implicitly a metric for so-called query and supp...
To overcome this challenge, few-shot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of few-shot learning methods on satellite-based datasets, little attention has been paid to exploring the applications of ...
Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dia
Paper tables with annotated results for How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning