{"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/residual-feature-distillation-network-for","title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","arxiv_id":"2009.11551","date":"2020-09-24","proceeding":null,"authors":["Jie Liu","Jie Tang","Gangshan Wu"],"abstract":"Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices due to the requirement of heavy computation. To solve this problem, various fast and lightweight CNN models have been proposed. The information distillation network is one of the state-of-the-art methods, which adopts the channel splitting operation to extract distilled features. However, it is not clear enough how this operation helps in the design of efficient SISR models. In this paper, we propose the feature distillation connection (FDC) that is functionally equivalent to the channel splitting operation while being more lightweight and flexible. Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN). RFDN uses multiple feature distillation connections to learn more discriminative feature representations. We also propose a shallow residual block (SRB) as the main building block of RFDN so that the network can benefit most from residual learning while still being lightweight enough. Extensive experimental results show that the proposed RFDN achieve a better trade-off against the state-of-the-art methods in terms of performance and model complexity. Moreover, we propose an enhanced RFDN (E-RFDN) and won the first place in the AIM 2020 efficient super-resolution challenge. Code will be available at https://github.com/njulj/RFDN.","url_abs":"https://arxiv.org/abs/2009.11551v1","url_pdf":"https://arxiv.org/pdf/2009.11551v1.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":"residual-feature-distillation-network-for","repo_url":"https://github.com/njulj/RFDN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"residual-feature-distillation-network-for","repo_url":"https://github.com/tuvovan/RFDNet-ImageSuperResolution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2009.11551","atlas_url":"https://app.syntology.ai/?focus=2009.11551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.11551"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tuvovan/RFDNet-ImageSuperResolution","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/njulj/RFDN","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":4,"ran_violates":1,"ran_draft_wrong":2,"unverified":2},"by_repo_kind":{"official":{"samples":6,"ran":4,"repositories":1},"listed":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"d3a234f32905b183","entry":"conv_layer","repo":"njulj/RFDN","repo_kind":"official","path":"block.py","file_url":"https://github.com/njulj/RFDN/blob/HEAD/block.py","link_basis":"plan_row","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d3a234f32905b183"}},{"code_sha256_prefix":"ab4109634b75ef8b","entry":"is_image_file","repo":"njulj/RFDN","repo_kind":"official","path":"utils/utils_image.py","file_url":"https://github.com/njulj/RFDN/blob/HEAD/utils/utils_image.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"code_sha256_prefix":"b6a238bb90c0069b","entry":"norm","repo":"njulj/RFDN","repo_kind":"official","path":"block.py","file_url":"https://github.com/njulj/RFDN/blob/HEAD/block.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b6a238bb90c0069b"}},{"code_sha256_prefix":"4a56ce1917d87f9b","entry":"pad","repo":"njulj/RFDN","repo_kind":"official","path":"block.py","file_url":"https://github.com/njulj/RFDN/blob/HEAD/block.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4a56ce1917d87f9b"}},{"code_sha256_prefix":"e8527b9fdd187d26","entry":"process_input","repo":"tuvovan/RFDNet-ImageSuperResolution","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/tuvovan/RFDNet-ImageSuperResolution/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e8527b9fdd187d26"}},{"code_sha256_prefix":"4fe5df3813972542","entry":"process_target","repo":"tuvovan/RFDNet-ImageSuperResolution","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/tuvovan/RFDNet-ImageSuperResolution/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4fe5df3813972542"}},{"code_sha256_prefix":"7944e373e6303444","entry":"scaling","repo":"tuvovan/RFDNet-ImageSuperResolution","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/tuvovan/RFDNet-ImageSuperResolution/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7944e373e6303444"}},{"code_sha256_prefix":"f7c097a7aced711b","entry":"get_image_paths","repo":"njulj/RFDN","repo_kind":"official","path":"utils/utils_image.py","file_url":"https://github.com/njulj/RFDN/blob/HEAD/utils/utils_image.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f7c097a7aced711b"}},{"code_sha256_prefix":"72d98b47ea73bd56","entry":"imread_uint","repo":"njulj/RFDN","repo_kind":"official","path":"utils/utils_image.py","file_url":"https://github.com/njulj/RFDN/blob/HEAD/utils/utils_image.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"72d98b47ea73bd56"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}