{"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/improved-implicity-neural-representation-with","title":"Improved Implicit Neural Representation with Fourier Reparameterized Training","arxiv_id":"2401.07402","date":"2024-01-15","proceeding":"CVPR 2024 1","authors":["Kexuan Shi","Xingyu Zhou","Shuhang Gu"],"abstract":"Implicit Neural Representation (INR) as a mighty representation paradigm has achieved success in various computer vision tasks recently. Due to the low-frequency bias issue of vanilla multi-layer perceptron (MLP), existing methods have investigated advanced techniques, such as positional encoding and periodic activation function, to improve the accuracy of INR. In this paper, we connect the network training bias with the reparameterization technique and theoretically prove that weight reparameterization could provide us a chance to alleviate the spectral bias of MLP. Based on our theoretical analysis, we propose a Fourier reparameterization method which learns coefficient matrix of fixed Fourier bases to compose the weights of MLP. We evaluate the proposed Fourier reparameterization method on different INR tasks with various MLP architectures, including vanilla MLP, MLP with positional encoding and MLP with advanced activation function, etc. The superiority approximation results on different MLP architectures clearly validate the advantage of our proposed method. Armed with our Fourier reparameterization method, better INR with more textures and less artifacts can be learned from the training data.","url_abs":"https://arxiv.org/abs/2401.07402v4","url_pdf":"https://arxiv.org/pdf/2401.07402v4.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":"improved-implicity-neural-representation-with","repo_url":"https://github.com/labshuhanggu/fr-inr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"improved-implicity-neural-representation-with","repo_url":"https://github.com/labshuhanggu/improved-implicity-neural-representation-with-fourier-bases-reparameterized-training","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2401.07402","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.07402"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/labshuhanggu/fr-inr","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/labshuhanggu/improved-implicity-neural-representation-with-fourier-bases-reparameterized-training","reach":{"status":"ok"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/LabShuHangGU/FR-INR","reach":{"status":"ok"}}],"summary":{"ran":4,"ran_draft_wrong":2,"ran_fixture":1,"unverified":1},"by_repo_kind":{"official":{"samples":8,"ran":7,"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":8,"samples":[{"code_sha256_prefix":"af8463e37aae5468","entry":"Fourier_reparam_linear","repo":"LabShuHangGU/FR-INR","repo_kind":"official","path":"modules.py","file_url":"https://github.com/LabShuHangGU/FR-INR/blob/HEAD/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"af8463e37aae5468"}},{"code_sha256_prefix":"fbdf22b2898acc80","entry":"get_coords","repo":"labshuhanggu/fr-inr","repo_kind":"official","path":"utlis.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/utlis.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fbdf22b2898acc80"}},{"code_sha256_prefix":"697b2e9f7ea406e3","entry":"get_image_tensor","repo":"labshuhanggu/fr-inr","repo_kind":"official","path":"utlis.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/utlis.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"697b2e9f7ea406e3"}},{"code_sha256_prefix":"43664adabad23284","entry":"get_mgrid","repo":"labshuhanggu/fr-inr","repo_kind":"official","path":"utlis.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/utlis.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"43664adabad23284"}},{"code_sha256_prefix":"049307a781780caa","entry":"get_rays","repo":"labshuhanggu/fr-inr","repo_kind":"official","path":"dvgo.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/dvgo.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"049307a781780caa"}},{"code_sha256_prefix":"797ecc3ad3e96c73","entry":"get_rays_np","repo":"labshuhanggu/fr-inr","repo_kind":"official","path":"dvgo.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/dvgo.py","link_basis":"plan_row","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"797ecc3ad3e96c73"}},{"code_sha256_prefix":"4366d225ac2fe16d","entry":"ndc_rays","repo":"labshuhanggu/fr-inr","repo_kind":"official","path":"dvgo.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/dvgo.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"code_sha256_prefix":"8c78e7aa4e20c812","entry":"get_INR","repo":"labshuhanggu/fr-inr","repo_kind":"official","path":"modules.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/modules.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8c78e7aa4e20c812"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}