{"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/aggregated-learning-a-vector-quantization","title":"Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers","arxiv_id":"2001.03955","date":"2020-01-12","proceeding":null,"authors":["Masoumeh Soflaei","Hongyu Guo","Ali Al-Bashabsheh","Yongyi Mao","Richong Zhang"],"abstract":"We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call \"IB learning\". We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a \"vector quantization\" approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, \"Aggregated Learning\", for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.","url_abs":"https://arxiv.org/abs/2001.03955v3","url_pdf":"https://arxiv.org/pdf/2001.03955v3.pdf","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":"aggregated-learning-a-vector-quantization","repo_url":"https://github.com/SITE5039/AgrLearn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"aggregated-learning","method_name":"Aggregated Learning"}],"datasets_introduced":[],"methods_introduced":[{"slug":"aggregated-learning","name":"Aggregated Learning","full_name":"Aggregated Learning"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2001.03955","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}