Shot Learning
Few-shot learning (FSL) aims to train machine learning models that can effectively learn new concepts or tasks from only a small number of examples, addressing the limitations of traditional methods requiring massive datasets. Current research focuses on improving model robustness to noisy data and heterogeneous tasks, exploring architectures like prototypical networks and meta-learning algorithms, and leveraging large vision-language models and external memory for enhanced performance. This field is crucial for advancing AI in data-scarce domains like medical image analysis and personalized medicine, where acquiring large labeled datasets is often impractical or impossible. The development of efficient and reliable FSL methods has significant implications for various applications, including object detection, natural language processing, and other areas where labeled data is limited.
Papers
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
Duong H. Le, Khoi D. Nguyen, Khoi Nguyen, Quoc-Huy Tran, Rang Nguyen, Binh-Son Hua
Metric Based Few-Shot Graph Classification
Donato Crisostomi, Simone Antonelli, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà
FHIST: A Benchmark for Few-shot Classification of Histological Images
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi, Ivaxi Sheth, Mohammad Havaei, Ismail Ben Ayed, Samira Ebrahimi Kahou
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks
Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida
Few-Shot Diffusion Models
Giorgio Giannone, Didrik Nielsen, Ole Winther
Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models
Mengzhou Xia, Mikel Artetxe, Jingfei Du, Danqi Chen, Ves Stoyanov
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification
Zaiyun Yang