Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions — Paper

Abstract

Few-shot learning is commonly evaluated under protocols that pre-train a model on a large auxiliary set whose classes are disjoint from the target episodes yet drawn from the same visual domain. This paper examines whether such protocols truly reflect low-data learning. We systematically compare no pre-training, class-disjoint in-domain pre-training, supervised out-of-domain pre-training, and label-free out-of-domain pre-training across eight datasets, three few-shot architectures, and multiple way-shot settings. Our results show that class disjointness alone is insufficient to remove the influence of target-domain data. In-domain pre-training improves over no pre-training by 33.41 percentage points on average, whereas supervised out-of-domain pre-training yields 23.75 percentage points, revealing a 9.66-point optimistic bias associated with domain overlap. Although out-of-domain pre-training is more realistic in applications where target-domain data are scarce, its effectiveness depends strongly on the compatibility between source and target domains. We further show that labeled source data are not strictly required, with an augmentation-based label-free strategy reaching an average gain of 27.71 percentage points and closely matching supervised out-of-domain pre-training at 27.97 percentage points. Finally, we introduce a descriptor-based source-selection strategy that estimates source-domain suitability before pre-training, reaching a median gap of only 1.37 percentage points to oracle selection. These findings highlight the need to move beyond in-domain pre-training as the default few-shot evaluation protocol, since it can overestimate performance in realistic scenarios where target-domain data are scarce.

Authors 3

Alejandro Galan-Cuenca, Marcelo Saval-Calvo, Antonio Javier Gallego

Specification

Official page: arxiv.org/abs/2609.10851
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
arXiv id: 2609.10851
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Categories: cs.CV, cs.AI, cs.LG
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
PDF: arxiv.org/pdf/2609.10851
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh

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9

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T19

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1 h ago

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Authors: Alejandro Galan-Cuenca, Marcelo Saval-Calvo, Antonio Javier Gallego

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Value	Valid from → to	Status	Source	Confidence	Extractor
2609.10851	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
Property changedPaperAre We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions
Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxiv
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11 Sept 2026
New paperPaperAre We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions
New paper: Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	1 h ago	1
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	1 h ago	1

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