{"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/a-closer-look-at-few-shot-classification-1","title":"A Closer Look at Few-shot Classification","arxiv_id":"1904.04232","date":"2019-04-08","proceeding":"ICLR 2019 5","authors":["Wei-Yu Chen","Yen-Cheng Liu","Zsolt Kira","Yu-Chiang Frank Wang","Jia-Bin Huang"],"abstract":"Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this paper, we present 1) a consistent comparative analysis of several representative few-shot classification algorithms, with results showing that deeper backbones significantly reduce the performance differences among methods on datasets with limited domain differences, 2) a modified baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the \\miniI and the CUB datasets, and 3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classification algorithms. Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones. In a realistic cross-domain evaluation setting, we show that a baseline method with a standard fine-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.","url_abs":"https://arxiv.org/abs/1904.04232v2","url_pdf":"https://arxiv.org/pdf/1904.04232v2.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":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/wyharveychen/CloserLookFewShot","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/Lieberk/Paddle-FSL-Baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/anujinho/trident","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/caesarea38/doclangid","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/cyvius96/few-shot-meta-baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/hu-my/taskattributedistance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/mikehuisman/revisiting-learned-optimizers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/tjujianyu/rrl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/vinuni-vishc/few-shot-transformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/yinboc/few-shot-meta-baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/neverCV/Baseline_FSL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok","spdx":"AGPL-3.0"}},{"paper_slug":"a-closer-look-at-few-shot-classification-1","repo_url":"https://github.com/sicara/easy-few-shot-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-4","task":"Few-Shot Image Classification","dataset":"Dirichlet CUB-200 (5-way, 1-shot)","model":"Baseline++","rank_in_archive_order":6,"of":8,"metrics":{"1:1 Accuracy":"69.4"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-5","task":"Few-Shot Image Classification","dataset":"Dirichlet CUB-200 (5-way, 5-shot)","model":"Baseline++","rank_in_archive_order":5,"of":8,"metrics":{"1:1 Accuracy":"87.5"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet","task":"Few-Shot Image Classification","dataset":"Dirichlet Mini-Imagenet (5-way, 1-shot)","model":"Baseline ++","rank_in_archive_order":7,"of":12,"metrics":{"1:1 Accuracy":"60.4"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-1","task":"Few-Shot Image Classification","dataset":"Dirichlet Mini-Imagenet (5-way, 5-shot)","model":"Baseline++","rank_in_archive_order":6,"of":12,"metrics":{"1:1 Accuracy":"79.7"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-2","task":"Few-Shot Image Classification","dataset":"Dirichlet Tiered-Imagenet (5-way, 1-shot)","model":"Baseline++","rank_in_archive_order":7,"of":9,"metrics":{"1:1 Accuracy":"68.0"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-3","task":"Few-Shot Image Classification","dataset":"Dirichlet Tiered-Imagenet (5-way, 5-shot)","model":"Baseline++","rank_in_archive_order":7,"of":9,"metrics":{"1:1 Accuracy":"84.2"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-5","task":"Few-Shot Image Classification","dataset":"Mini-ImageNet-CUB 5-way (1-shot)","model":"Baseline++ (Chen et al., 2019)","rank_in_archive_order":12,"of":12,"metrics":{"Accuracy":"33.04"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-6","task":"Few-Shot Image Classification","dataset":"Mini-ImageNet-CUB 5-way (5-shot)","model":"Baseline++ (Chen et al., 2019)","rank_in_archive_order":6,"of":8,"metrics":{"Accuracy":"62.04"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.04232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04232"}},"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/hu-my/taskattributedistance","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cyvius96/few-shot-meta-baseline","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/anujinho/trident","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sicara/easy-few-shot-learning","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/neverCV/Baseline_FSL","reach":{"status":"ok","spdx":"AGPL-3.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vinuni-vishc/few-shot-transformer","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/caesarea38/doclangid","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wyharveychen/CloserLookFewShot","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yinboc/few-shot-meta-baseline","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mikehuisman/revisiting-learned-optimizers","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tjujianyu/rrl","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Lieberk/Paddle-FSL-Baseline","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran_draft_wrong":4,"ran_honours":1,"ran_violates":1,"ran_fixture":1,"unverified":1},"by_repo_kind":{"listed":{"samples":6,"ran":5,"repositories":4}},"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":7,"samples":[{"code_sha256_prefix":"021c112dc2125292","entry":"change_model","repo":"vinuni-vishc/Few-Shot-Cosine-Transformer","repo_kind":"listed","path":"train_test.py","file_url":"https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer/blob/HEAD/train_test.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"021c112dc2125292"}},{"code_sha256_prefix":"d9def42110729a85","entry":"conv1x1","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"d9def42110729a85"}},{"code_sha256_prefix":"160bb14bd76201b4","entry":"conv3x3","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"code_sha256_prefix":"5c27b393f7f2a9a1","entry":"direct_test","repo":"vinuni-vishc/Few-Shot-Cosine-Transformer","repo_kind":"listed","path":"train_test.py","file_url":"https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer/blob/HEAD/train_test.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"5c27b393f7f2a9a1"}},{"code_sha256_prefix":"3233bdb56ef8b4da","entry":"mean_confidence_interval","repo":"yinboc/few-shot-meta-baseline","repo_kind":"listed","path":"test_few_shot.py","file_url":"https://github.com/yinboc/few-shot-meta-baseline/blob/HEAD/test_few_shot.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3233bdb56ef8b4da"}},{"code_sha256_prefix":"55beb8cfed8198d9","entry":"train","repo":"vinuni-vishc/Few-Shot-Cosine-Transformer","repo_kind":"listed","path":"train_test.py","file_url":"https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer/blob/HEAD/train_test.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"55beb8cfed8198d9"}},{"code_sha256_prefix":"70cf13356ae5b0bd","entry":"transform_model_input_data_type","repo":"tjujianyu/rrl","repo_kind":"listed","path":"src/models.py","file_url":"https://github.com/tjujianyu/rrl/blob/HEAD/src/models.py","link_basis":"first_harvest_node","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":"70cf13356ae5b0bd"}},{"code_sha256_prefix":"5290c92ce05cddca","entry":"feature_evaluation_with_dists","repo":"hu-my/taskattributedistance","repo_kind":"listed","path":"plot_distance_acc.py","file_url":"https://github.com/hu-my/taskattributedistance/blob/HEAD/plot_distance_acc.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":"5290c92ce05cddca"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}