{"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/optimizing-neural-networks-with-gradient-1","title":"Optimizing Neural Networks with Gradient Lexicase Selection","arxiv_id":"2312.12606","date":"2023-12-19","proceeding":"ICLR 2022 4","authors":["Li Ding","Lee Spector"],"abstract":"One potential drawback of using aggregated performance measurement in machine learning is that models may learn to accept higher errors on some training cases as compromises for lower errors on others, with the lower errors actually being instances of overfitting. This can lead to both stagnation at local optima and poor generalization. Lexicase selection is an uncompromising method developed in evolutionary computation, which selects models on the basis of sequences of individual training case errors instead of using aggregated metrics such as loss and accuracy. In this paper, we investigate how lexicase selection, in its general form, can be integrated into the context of deep learning to enhance generalization. We propose Gradient Lexicase Selection, an optimization framework that combines gradient descent and lexicase selection in an evolutionary fashion. Our experimental results demonstrate that the proposed method improves the generalization performance of various widely-used deep neural network architectures across three image classification benchmarks. Additionally, qualitative analysis suggests that our method assists networks in learning more diverse representations. Our source code is available on GitHub: https://github.com/ld-ing/gradient-lexicase.","url_abs":"https://arxiv.org/abs/2312.12606v1","url_pdf":"https://arxiv.org/pdf/2312.12606v1.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":"optimizing-neural-networks-with-gradient-1","repo_url":"https://github.com/ld-ing/gradient-lexicase","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2312.12606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12606"}},"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":"deterministic:regex_extraction","url":"https://github.com/ld-ing/gradient-lexicase","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":6},"by_repo_kind":{"official":{"samples":6,"ran":6,"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":"dfb395bae3d07d76","entry":"DenseNet121","repo":"ld-ing/gradient-lexicase","repo_kind":"official","path":"models/densenet.py","file_url":"https://github.com/ld-ing/gradient-lexicase/blob/HEAD/models/densenet.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":"dfb395bae3d07d76"}},{"code_sha256_prefix":"f295fe3c209ecf78","entry":"DenseNet169","repo":"ld-ing/gradient-lexicase","repo_kind":"official","path":"models/densenet.py","file_url":"https://github.com/ld-ing/gradient-lexicase/blob/HEAD/models/densenet.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":"f295fe3c209ecf78"}},{"code_sha256_prefix":"6383aee1c940e7a5","entry":"DenseNet201","repo":"ld-ing/gradient-lexicase","repo_kind":"official","path":"models/densenet.py","file_url":"https://github.com/ld-ing/gradient-lexicase/blob/HEAD/models/densenet.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":"6383aee1c940e7a5"}},{"code_sha256_prefix":"f7b96dbdddb56a68","entry":"EfficientNetB0","repo":"ld-ing/gradient-lexicase","repo_kind":"official","path":"models/efficientnet.py","file_url":"https://github.com/ld-ing/gradient-lexicase/blob/HEAD/models/efficientnet.py","link_basis":"first_harvest_node","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":"f7b96dbdddb56a68"}},{"code_sha256_prefix":"4304a326c593f8db","entry":"drop_connect","repo":"ld-ing/gradient-lexicase","repo_kind":"official","path":"models/efficientnet.py","file_url":"https://github.com/ld-ing/gradient-lexicase/blob/HEAD/models/efficientnet.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":"4304a326c593f8db"}},{"code_sha256_prefix":"8737c82de631cffc","entry":"swish","repo":"ld-ing/gradient-lexicase","repo_kind":"official","path":"models/efficientnet.py","file_url":"https://github.com/ld-ing/gradient-lexicase/blob/HEAD/models/efficientnet.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":"8737c82de631cffc"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}