← Long-tail Learning

Papers with Code · CC BY-SA 4.0

iNaturalist 2018

아카이브 평가 43건 · 마지막 적재/변경 2026-10-30

원본 데이터셋 링크 1

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
LDAM-DRS-RSG	Top-1 Accuracy 70.3%	RSG: A Simple but Effective Module for Learning Imbalanced Datasets 2021-06-18	코드 1
IEM	Top-1 Accuracy 70.2	Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition 2020-06-01	연결 없음
LTR-weight-balancing(ResNet-50)	Top-1 Accuracy 70.2%	Long-Tailed Recognition via Weight Balancing 2022-03-27	코드 1 · 코드 2
LDAM + DRW + SAM	Top-1 Accuracy 70.1	Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data 2022-12-28	코드 1
LADE	Top-1 Accuracy 70.0%	Disentangling Label Distribution for Long-tailed Visual Recognition 2020-12-01	코드 1 · 코드 2
TSC(ResNet-50)	Top-1 Accuracy 69.7%	Targeted Supervised Contrastive Learning for Long-Tailed Recognition 2021-11-27	코드 1
CB-LWS	Top-1 Accuracy 69.5%	Decoupling Representation and Classifier for Long-Tailed Recognition 2019-10-21	코드 1 · 코드 2 · 코드 3 · 코드 4
MetaSAug	Top-1 Accuracy 68.75%	MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition 2021-03-23	코드 1
cRT + SSP	Top-1 Accuracy 68.1%	Rethinking the Value of Labels for Improving Class-Imbalanced Learning 2020-06-13	코드 1
Hybrid-PSC	Top-1 Accuracy 68.1%	Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification 2021-03-26	연결 없음
CE-DRW-IC	Top-1 Accuracy 67.9%	Posterior Re-calibration for Imbalanced Datasets 2020-10-22	연결 없음
Domain Adaptation	Top-1 Accuracy 67.5%	Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective 2020-03-24	코드 1
Online Feature Augmentation	Top-1 Accuracy 65.9%	Feature Space Augmentation for Long-Tailed Data 2020-08-09	연결 없음