{"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/push-the-student-to-learn-right-progressive","title":"Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting","arxiv_id":"1902.07379","date":"2019-02-20","proceeding":"NeurIPS 2019 12","authors":["Jun Shu","Qi Xie","Lixuan Yi","Qian Zhao","Sanping Zhou","Zongben Xu","Deyu Meng"],"abstract":"Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to alleviate this issue by designing a weighting function mapping from training loss to sample weight, and then iterating between weight recalculating and classifier updating. Current approaches, however, need manually pre-specify the weighting function as well as its additional hyper-parameters. It makes them fairly hard to be generally applied in practice due to the significant variation of proper weighting schemes relying on the investigated problem and training data. To address this issue, we propose a method capable of adaptively learning an explicit weighting function directly from data. The weighting function is an MLP with one hidden layer, constituting a universal approximator to almost any continuous functions, making the method able to fit a wide range of weighting functions including those assumed in conventional research. Guided by a small amount of unbiased meta-data, the parameters of the weighting function can be finely updated simultaneously with the learning process of the classifiers. Synthetic and real experiments substantiate the capability of our method for achieving proper weighting functions in class imbalance and noisy label cases, fully complying with the common settings in traditional methods, and more complicated scenarios beyond conventional cases. This naturally leads to its better accuracy than other state-of-the-art methods.","url_abs":"https://arxiv.org/abs/1902.07379v6","url_pdf":"https://arxiv.org/pdf/1902.07379v6.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":"push-the-student-to-learn-right-progressive","repo_url":"https://github.com/xjtushujun/meta-weight-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"push-the-student-to-learn-right-progressive","repo_url":"https://github.com/arghosh/noisy_label_pretrain","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"push-the-student-to-learn-right-progressive","repo_url":"https://github.com/shiyunyi/meta-weight-net_code-optimization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-clothing1m","task":"Image Classification","dataset":"Clothing1M","model":"MW-Net","rank_in_archive_order":25,"of":51,"metrics":{"Accuracy":"73.72%"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1902.07379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07379"}},"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/xjtushujun/meta-weight-net","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/arghosh/noisy_label_pretrain","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/shiyunyi/meta-weight-net_code-optimization","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":2,"unverified":9},"by_repo_kind":{"official":{"samples":6,"ran":2,"repositories":1},"listed":{"samples":5,"ran":0,"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":"21adbeb5db124ddb","entry":"to_var","repo":"xjtushujun/meta-weight-net","repo_kind":"official","path":"train_WRN-28-10_Meta_PGC.py","file_url":"https://github.com/xjtushujun/meta-weight-net/blob/HEAD/train_WRN-28-10_Meta_PGC.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"21adbeb5db124ddb"}},{"code_sha256_prefix":"a2f6a25906e664c5","entry":"to_var","repo":"xjtushujun/meta-weight-net","repo_kind":"official","path":"resnet.py","file_url":"https://github.com/xjtushujun/meta-weight-net/blob/HEAD/resnet.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a2f6a25906e664c5"}},{"code_sha256_prefix":"f0c9a29156911331","entry":"accuracy","repo":"xjtushujun/meta-weight-net","repo_kind":"official","path":"MW-Net.py","file_url":"https://github.com/xjtushujun/meta-weight-net/blob/HEAD/MW-Net.py","link_basis":"plan_row","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":"f0c9a29156911331"}},{"code_sha256_prefix":"032cefe492571991","entry":"compute_loss_accuracy","repo":"shiyunyi/meta-weight-net_code-optimization","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/shiyunyi/meta-weight-net_code-optimization/blob/HEAD/utils.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":"032cefe492571991"}},{"code_sha256_prefix":"abf194351265229d","entry":"flip1_corruption","repo":"shiyunyi/meta-weight-net_code-optimization","repo_kind":"listed","path":"noisy_long_tail_CIFAR.py","file_url":"https://github.com/shiyunyi/meta-weight-net_code-optimization/blob/HEAD/noisy_long_tail_CIFAR.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":"abf194351265229d"}},{"code_sha256_prefix":"cf33e751c50776c4","entry":"flip2_corruption","repo":"shiyunyi/meta-weight-net_code-optimization","repo_kind":"listed","path":"noisy_long_tail_CIFAR.py","file_url":"https://github.com/shiyunyi/meta-weight-net_code-optimization/blob/HEAD/noisy_long_tail_CIFAR.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":"cf33e751c50776c4"}},{"code_sha256_prefix":"74fa122d15c0f3ce","entry":"flip_labels_C","repo":"xjtushujun/meta-weight-net","repo_kind":"official","path":"load_corrupted_data.py","file_url":"https://github.com/xjtushujun/meta-weight-net/blob/HEAD/load_corrupted_data.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":"74fa122d15c0f3ce"}},{"code_sha256_prefix":"394e8668227b39c9","entry":"flip_labels_C_two","repo":"xjtushujun/meta-weight-net","repo_kind":"official","path":"load_corrupted_data.py","file_url":"https://github.com/xjtushujun/meta-weight-net/blob/HEAD/load_corrupted_data.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":"394e8668227b39c9"}},{"code_sha256_prefix":"9b344295bdd3919b","entry":"stop_epoch","repo":"shiyunyi/meta-weight-net_code-optimization","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/shiyunyi/meta-weight-net_code-optimization/blob/HEAD/utils.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":"9b344295bdd3919b"}},{"code_sha256_prefix":"01ac6504b411b04b","entry":"uniform_corruption","repo":"shiyunyi/meta-weight-net_code-optimization","repo_kind":"listed","path":"noisy_long_tail_CIFAR.py","file_url":"https://github.com/shiyunyi/meta-weight-net_code-optimization/blob/HEAD/noisy_long_tail_CIFAR.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":"01ac6504b411b04b"}},{"code_sha256_prefix":"ba6749b25e502f54","entry":"uniform_mix_C","repo":"xjtushujun/meta-weight-net","repo_kind":"official","path":"load_corrupted_data.py","file_url":"https://github.com/xjtushujun/meta-weight-net/blob/HEAD/load_corrupted_data.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":"ba6749b25e502f54"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}