{"url":"/method/scnet","slug":"scnet","name":"SCNet","full_name":"SCNet","full_name_withheld":false,"description_markdown":"**Sample Consistency Network (SCNet)** is a method for instance segmentation which ensures the IoU distribution of the samples at training time are as close to that at inference time. To this end, only the outputs of the last box stage are used for mask predictions at both training and inference. The Figure shows the IoU distribution of the samples going to the mask branch at training time with/without sample consistency compared to that at inference time.","description_state":"present","introduced_year":null,"introduced_by":{"title":"SCNet: Training Inference Sample Consistency for Instance Segmentation","paper":"/paper/scnet-training-inference-sample-consistency","first_author":"Thang Vu","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/scnet-training-inference-sample-consistency"},"source":{"url":"https://arxiv.org/abs/2012.10150v1","title":"SCNet: Training Inference Sample Consistency for Instance Segmentation","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Instance Segmentation Models","url":"/methods/category/instance-segmentation-models","pwc_aliases":[]}],"n_papers_tagged":6,"archive_num_papers":6,"papers_newest_first":[{"paper":null,"title":"Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification","date":"2024-11-01","arxiv_id":"2411.00330","n_code_links":0,"syntology":null},{"paper":"/paper/scnet-sparse-compression-network-for-music","title":"SCNet: Sparse Compression Network for Music Source Separation","date":"2024-01-24","arxiv_id":"2401.13276","n_code_links":3,"syntology":null},{"paper":"/paper/semantic-aware-consistency-network-for-cloth","title":"Semantic-aware Consistency Network for Cloth-changing Person Re-Identification","date":"2023-08-27","arxiv_id":"2308.14113","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-scalable-deep-compressed","title":"Self-Supervised Scalable Deep Compressed Sensing","date":"2023-08-26","arxiv_id":"2308.13777","n_code_links":1,"syntology":null},{"paper":"/paper/underwater-image-enhancement-via-learning","title":"Underwater Image Enhancement via Learning Water Type Desensitized Representations","date":"2021-02-01","arxiv_id":"2102.00676","n_code_links":1,"syntology":null},{"paper":"/paper/scnet-training-inference-sample-consistency","title":"SCNet: Training Inference Sample Consistency for Instance Segmentation","date":"2020-12-18","arxiv_id":"2012.10150","n_code_links":2,"syntology":null}],"papers_shown":6,"tasks":[{"task":"/task/cloth-changing-person-re-identification","name":"Cloth-Changing Person Re-Identification","papers":2},{"task":"/task/person-re-identification","name":"Person Re-Identification","papers":2},{"task":null,"name":"CPU","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/diversity","name":"Diversity","papers":1},{"task":"/task/image-enhancement","name":"Image Enhancement","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/music-source-separation","name":"Music Source Separation","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/prompt-learning","name":"Prompt Learning","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/type","name":"Vocal Bursts Type Prediction","papers":1},{"task":"/task/compressed-sensing","name":"compressed sensing","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":14,"n_tasks":14,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1},{"year":"2023","papers":2},{"year":"2024","papers":2}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/scnet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}