{"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/addressing-target-shift-in-zero-shot-learning","title":"Addressing target shift in zero-shot learning using grouped adversarial learning","arxiv_id":"2003.00845","date":"2020-03-02","proceeding":null,"authors":["Saneem Ahmed Chemmengath","Soumava Paul","Samarth Bharadwaj","Suranjana Samanta","Karthik Sankaranarayanan"],"abstract":"Zero-shot learning (ZSL) algorithms typically work by exploiting attribute correlations to be able to make predictions in unseen classes. However, these correlations do not remain intact at test time in most practical settings and the resulting change in these correlations lead to adverse effects on zero-shot learning performance. In this paper, we present a new paradigm for ZSL that: (i) utilizes the class-attribute mapping of unseen classes to estimate the change in target distribution (target shift), and (ii) propose a novel technique called grouped Adversarial Learning (gAL) to reduce negative effects of this shift. Our approach is widely applicable for several existing ZSL algorithms, including those with implicit attribute predictions. We apply the proposed technique ($g$AL) on three popular ZSL algorithms: ALE, SJE, and DEVISE, and show performance improvements on 4 popular ZSL datasets: AwA2, aPY, CUB and SUN. We obtain SOTA results on SUN and aPY datasets and achieve comparable results on AwA2.","url_abs":"https://arxiv.org/abs/2003.00845v2","url_pdf":"https://arxiv.org/pdf/2003.00845v2.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":"addressing-target-shift-in-zero-shot-learning","repo_url":"https://github.com/mvp18/gAL-MELEX","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.00845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.00845"}},"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/mvp18/gAL-MELEX","reach":null}],"summary":{"ran":2,"ran_draft_wrong":1,"ran_honours":1},"by_repo_kind":{"official":{"samples":4,"ran":4,"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":"e828fbdd41054ac5","entry":"GAL","repo":"mvp18/gAL-MELEX","repo_kind":"official","path":"APY/code/model.py","file_url":"https://github.com/mvp18/gAL-MELEX/blob/HEAD/APY/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e828fbdd41054ac5"}},{"code_sha256_prefix":"5a9a5eb577f20e70","entry":"GradReverse","repo":"mvp18/gAL-MELEX","repo_kind":"official","path":"APY/code/model.py","file_url":"https://github.com/mvp18/gAL-MELEX/blob/HEAD/APY/code/model.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":"5a9a5eb577f20e70"}},{"code_sha256_prefix":"2599ba4db1567616","entry":"Merge","repo":"mvp18/gAL-MELEX","repo_kind":"official","path":"APY/code/model.py","file_url":"https://github.com/mvp18/gAL-MELEX/blob/HEAD/APY/code/model.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":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2599ba4db1567616"}},{"code_sha256_prefix":"5a2b24f39d01d817","entry":"grad_reverse","repo":"mvp18/gAL-MELEX","repo_kind":"official","path":"APY/code/model.py","file_url":"https://github.com/mvp18/gAL-MELEX/blob/HEAD/APY/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5a2b24f39d01d817"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}