{"url":"/method/dime","slug":"dime","name":"DIME","full_name":"Distance to Modelled Embedding","full_name_withheld":false,"description_markdown":"**DIME**, or **Distance to Modelled Embedding**, is a method for detecting out-of-distribution examples during prediction time. Given a trained neural network, the training data drawn from some high-dimensional distribution in data space $X$ is transformed into the model’s intermediate feature vector space $\\mathbb{R}^{p}$. The training set embedding is linearly approximated as a hyperplane. When we then receive new observations it is difficult to assess if observations are out-of-distribution directly in data space, so we transform them into the same intermediate feature space. Finally, the Distance-to-Modelled-Embedding (DIME) can be used to assess whether new observations fit into the expected embedding covariance structure.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2108.10673v1","title":"Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Out-of-Distribution Example Detection","url":"/methods/category/out-of-distribution-example-detection","pwc_aliases":[]}],"n_papers_tagged":5,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"DIME:Diffusion-Based Maximum Entropy Reinforcement Learning","date":"2025-02-04","arxiv_id":"2502.02316","n_code_links":0,"syntology":null},{"paper":null,"title":"Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation","date":"2022-03-24","arxiv_id":"2203.13251","n_code_links":0,"syntology":null},{"paper":"/paper/dime-fine-grained-interpretations-of","title":"DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations","date":"2022-03-03","arxiv_id":"2203.02013","n_code_links":1,"syntology":{"ran":5,"of":7,"unverified":2,"pointer_only":0}},{"paper":"/paper/rethnicity-predicting-ethnicity-from-names","title":"Rethnicity: Predicting Ethnicity from Names","date":"2021-09-19","arxiv_id":"2109.09228","n_code_links":1,"syntology":null},{"paper":"/paper/out-of-distribution-example-detection-in-deep","title":"Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding","date":"2021-08-24","arxiv_id":"2108.10673","n_code_links":1,"syntology":null}],"papers_shown":5,"tasks":[{"task":"/task/decision-making","name":"Decision Making","papers":1},{"task":"/task/disentanglement","name":"Disentanglement","papers":1},{"task":"/task/imitation-learning","name":"Imitation Learning","papers":1},{"task":"/task/prediction","name":"Prediction","papers":1},{"task":"/task/reinforcement-learning","name":"Reinforcement Learning","papers":1},{"task":"/task/time-series-1","name":"Time Series","papers":1},{"task":"/task/time-series","name":"Time Series Analysis","papers":1},{"task":"/task/reinforcement-learning-2","name":"reinforcement-learning","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2021","papers":2},{"year":"2022","papers":2},{"year":"2025","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/dime"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}