{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-meta-learning-via-latent-space","title":"Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling","arxiv_id":null,"date":"2021-09-30","proceeding":"5th Workshop on Meta-Learning at NeurIPS 2021 2021 9","authors":["Deqian Kong","Bo Pang","Ying Nian Wu"],"abstract":"Meta-learning aims to learn a model from a stream of tasks such that the model is able to generalize across tasks and rapidly adapt to new tasks. We propose to learn an energy-based model (EBM) in the latent space of a top-down generative\r\nmodel such that the EBM in the low dimensional latent space is able to be learned efficiently and adapt to each task rapidly. Furthermore, the energy term couples a continuous latent vector and a symbolic one-hot label. Such coupling formulation allows the model to be learned in an unsupervised manner when the labels are unknown. Our model is learned unsupervisedly in the meta-training phase and evaluated semi-supervisedly in the meta-test phase. We evaluate our model on widely used benchmarks for few-shot meta-learning, Omniglot, and Mini-ImageNet. Our model achieves competitive or superior performance compared to previous state-of-the-art meta-learning models.","url_abs":"https://openreview.net/forum?id=-pLftu7EpXz","url_pdf":"https://openreview.net/pdf?id=-pLftu7EpXz","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"unsupervised-few-shot-image-classification","task_name":"Unsupervised Few-Shot Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-few-shot-image-classification-on","task":"Unsupervised Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (1-shot)","model":"Meta-SVEBM","rank_in_archive_order":19,"of":28,"metrics":{"Accuracy":"43.38"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-few-shot-image-classification-on-1","task":"Unsupervised Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (5-shot)","model":"Meta-SVEBM","rank_in_archive_order":19,"of":28,"metrics":{"Accuracy":"58.03"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}