atlas

Research map / Label-efficient and robust learning / Test-time adaptation

Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification

NeurIPS 2023 · Label-efficient and robust learning › Test-time adaptation · approach: Extrapolation and single-sample adaptation · 3 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Combine independently trained coarse and fine predictions by multiplying their probabilitiesResNet-18 classifiers · iNaturalist-19Top-1 error (%): 36.44 ± 0.061 -> 35.33 ± 0.037Helped
Combine independently trained coarse and fine predictions by multiplying their probabilitiesResNet-18 classifiers · iNaturalist-19Mistake severity: 2.39 ± 0.007 -> 2.15 ± 0.003Helped
Combine independently trained coarse and fine predictions by multiplying their probabilitiesResNet-18 classifiers · tieredImageNet-HTop-1 error (%): 30.64 ± 0.030 -> 29.81 ± 0.081Helped
Combine independently trained coarse and fine predictions by multiplying their probabilitiesResNet-18 classifiers · tieredImageNet-HMistake severity: 7.07 ± 0.010 -> 6.95 ± 0.013Helped
Use HiE with MoCo-HMoCo-H · iNaturalist-19, 10 images/labelTop-1 accuracy: 51.23 -> 55.13Helped
Use HiE with CrossEntropyCrossEntropy · iNaturalist-19, 100 images/labelHierarchical Distance@1: 1.17 -> 0.89Helped
Replace the independently trained coarse classifier with coarse predictions marginalized from the fine model (ablation)HiE-Self · iNaturalist-19Top-1 error (%): 36.44 ± 0.061 -> 36.36 ± 0.047No clear effect
Add a hierarchical loss to marginalized coarse predictions (ablation)Cross-Entropy-H · iNaturalist-19Top-1 error (%): 36.44 ± 0.061 -> 37.63 ± 0.054Hurt
Use hierarchy levels {6, 7} rather than leaf level {7} alone (ablation)HiE · iNaturalist-19 speciesTop-1 accuracy: 63.63 -> 64.65Helped
Add level 5 to levels {6, 7} (ablation)HiE · iNaturalist-19 speciesTop-1 accuracy: 64.65 -> 64.72Helped
Add level 4 to levels {5 - 7} (ablation)HiE · iNaturalist-19 speciesTop-1 accuracy: 64.72 -> 64.48Hurt
Add level 3 to levels {4 - 7} (ablation)HiE · iNaturalist-19 speciesTop-1 accuracy: 64.48 -> 64.47No clear effect

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy