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Tiered ImageNet 10-way (5-shot) Benchmark (Few-Shot Image Classification)
Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)
The archive carries no text for this table; the description above is the archive's text for the task Few-Shot Image Classification. archive 2025-07-28
Over time archive 2025-07-28
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Direction inferred from the metric name, not from the archive: Accuracy (higher is better). Points are placed at the row's paper date; 13 of 13 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 | Transductive CNAPS + FETI | 80.6 | ✓ | Paper | Code | 2020 | 3 of 9 ran · 6 unverified | report |
| 2 | Simple CNAPS + FETI | 78.5 | ✓ | Paper | Code | 2019 | linked, not harvested | report |
| 3 | Transductive CNAPS | 72.5 | – | Paper | Code | 2020 | 3 of 9 ran · 6 unverified | report |
| 4 | Simple CNAPS | 70.2 | – | Paper | Code | 2019 | linked, not harvested | report |
| 5 | TPN (Higher Shot) | 59.4 | – | Paper | Code | 2018 | linked, not harvested | report |
| 6 | Prototypical Networks (Higher Way) | 58.3 | – | Paper | Code | 2017 | 49 of 64 ran · 15 unverified | report |
| 7 | Relation Networks | 58.0 | – | Paper | Code | 2017 | 1 of 2 ran · 1 unverified | report |
| 8 | Label Propagation | 57.9 | – | Paper | Code | 2018 | linked, not harvested | report |
| 9 | Prototypical Networks | 57.8 | – | Paper | Code | 2017 | 49 of 64 ran · 15 unverified | report |
| 10 | MAML + Transduction | 54.7 | – | Paper | Code | 2017 | 86 of 154 ran · 68 unverified | report |
| 11 | MAML | 53.3 | – | Paper | Code | 2017 | 86 of 154 ran · 68 unverified | report |
| 12 | Reptile+BN | 52.0 | – | Paper | Code | 2018 | 24 of 42 ran · 18 unverified | report |
| 13 | Reptile | 48.0 | – | Paper | Code | 2018 | 24 of 42 ran · 18 unverified | report |
All 13 rows shown. 13 link to a paper page on this site; 2 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). 9 rows have a graph line, from 5 distinct papers; 9 rows (5 papers) have at least one sample that ran. Counting each paper once: Syntology ran 163 of 271 samples; 108 unverified. Separately, 89 of those 271 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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