{"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/superclass-conditional-gaussian-mixture-model","title":"Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings","arxiv_id":null,"date":"2021-09-29","proceeding":"ICLR 2022 4","authors":["Jingchao Ni","Wei Cheng","Zhengzhang Chen","Takayoshi Asakura","Tomoya Soma","Sho Kato","Haifeng Chen"],"abstract":"Learning fine-grained embeddings is essential for extending the generalizability of models pretrained on \"coarsely\" annotated labels (e.g., animals). It is crucial to fields where fine-grained labeling (e.g., breeds) requires strong domain expertise thus is prohibitive, such as medicine, but predicting them is desirable. The dilemma necessitates the adaptation of a \"coarsely\" pretrained model to new tasks with a few unseen \"finer-grained\" training labels. However, pretraining with only coarse supervision tends to suppress intra-class variation, which is indispensable for cross-granularity adaptation. In this paper, we develop a training framework underlain by a novel superclass conditional Gaussian mixture model (SCGM). SCGM imitates the generative process of samples from hierarchies of classes by means of latent variable modeling of the superclass-subclass relationships. The framework is agnostic to the encoders and only adds a few distribution related parameters, thus is efficient, and flexible to different domains. The model parameters are learned end-to-end by maximum-likelihood estimation via an Expectation-Maximization algorithm in a principled manner. Extensive experimental results on benchmark datasets and a real-life medical dataset demonstrate the effectiveness of our method.","url_abs":"https://openreview.net/forum?id=vds4SNooOe","url_pdf":"https://openreview.net/pdf?id=vds4SNooOe","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":[{"paper_slug":"superclass-conditional-gaussian-mixture-model","repo_url":"https://github.com/KnowledgeDiscovery/SCGM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}