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Zero-Shot Learning archive 2025-07-28

CUB-200-2011 Benchmark (Zero-Shot Learning)

14 rows 7 with code listed 5 metrics Dataset page

Zero-shot learning (ZSL) is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning.

Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, language models can be evaluated on downstream tasks without fine tuning.

Benchmark datasets for zero-shot learning include aPY, AwA, and CUB, among others.

Further readings:

The archive carries no text for this table; the description above is the archive's text for the task Zero-Shot Learning. archive 2025-07-28

Over time archive 2025-07-28

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Direction inferred from the metric name, not from the archive: average top-1 classification accuracy (higher is better), Accuracy Seen (higher is better), Accuracy Unseen (higher is better), Accuracy (higher is better). Not inferred (points only, no best-so-far line): H. Points are placed at the row's paper date; 14 of 14 rows carry one.

Results archive 2025-07-28

Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.

Paper Code Ran Syntology Report
1 ZeroDiff 87.5 – Paper – 2024 no code linked report
2 DUET 72.372.862.967.5 – Paper Code 2022 linked, not harvested report
3 Composer 69.4 – Paper – 2021 no code linked report
4 HDC-ZSC-MLP 65.6 – Paper – 2024 no code linked report
5 ZSL_TF-VAEGAN 64.9 – Paper Code 2020 1 of 4 ran · 3 unverified report
6 HDC-ZSC 63.8 – Paper – 2024 no code linked report
7 SPOT 62.9 – Paper – 2023 no code linked report
8 f-VAEGAN-D2 61.0 – Paper – 2019 no code linked report
9 TCN 59.5 – Paper – 2019 no code linked report
10 LisGAN 58.8 – Paper Code 2019 1 of 1 ran · 0 unverified report
11 Cycle-WGAN 58.6 – Paper Code 2018 linked, not harvested report
12 f-CLSWGAN 57.3 – Paper Code 2017 linked, not harvested report
13 ZLaP 64.3 – Paper Code 2024 1 of 2 ran · 1 unverified report
14 ZLaP* 64.2 – Paper Code 2024 1 of 2 ran · 1 unverified report

All 14 rows shown. 14 link to a paper page on this site; 0 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28

Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 4 rows have a graph line, from 3 distinct papers; 4 rows (3 papers) have at least one sample that ran. Counting each paper once: Syntology ran 3 of 7 samples; 4 unverified. Separately, 1 of those 7 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.

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