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CIFAR-10

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아카이브 평가 40건 · 마지막 적재/변경 2026-10-30

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
TURTLE (CLIP + DINOv2)	Accuracy 0.995 NMI 0.985 ARI 0.989	Let Go of Your Labels with Unsupervised Transfer 2024-06-11	코드 1
PRCut (CLIP)	Accuracy 0.975 NMI 0.934	Deep Clustering via Probabilistic Ratio-Cut Optimization 2025-02-01	코드 1
PRO-DSC	Accuracy 0.972 NMI 0.928	Exploring a Principled Framework for Deep Subspace Clustering 2025-03-21	코드 1
TEMI CLIP ViT-L (openai)	Accuracy 0.969 NMI 0.926 Train set Train ARI 0.932 Backbone ViT-L	Exploring the Limits of Deep Image Clustering using Pretrained Models 2023-03-31	코드 1
DPAC	Accuracy 0.934 NMI 0.87 ARI 0.866 Backbone ResNet-34	Deep Online Probability Aggregation Clustering 2024-07-07	코드 1
SPICE-BPA	Accuracy 0.933 NMI 0.870 ARI 0.866 Backbone ResNet-18	The Balanced-Pairwise-Affinities Feature Transform 2024-06-25	코드 1
SeCu	Accuracy 0.93 NMI 0.861 Train set Train ARI 0.857 Backbone ResNet-18	Stable Cluster Discrimination for Deep Clustering 2023-11-24	코드 1
TAC	Accuracy 0.919 NMI 0.833 ARI 0.831	Image Clustering with External Guidance 2023-10-18	코드 1
SPICE*	Accuracy 0.918 NMI 0.850 Train set Train ARI 0.836 Backbone ResNet-18	SPICE: Semantic Pseudo-labeling for Image Clustering 2021-03-17	코드 1
DCN+BRB	Accuracy 0.912 NMI 0.837 Train set Train ARI 0.824 Backbone ResNet-18	Breaking the Reclustering Barrier in Centroid-based Deep Clustering 2024-11-04	코드 1
IDEC+BRB	Accuracy 0.907 NMI 0.833 Train set Train ARI 0.818 Backbone ResNet-18	Breaking the Reclustering Barrier in Centroid-based Deep Clustering 2024-11-04	코드 1
DEC+BRB	Accuracy 0.906 NMI 0.826 Train set Train ARI 0.812 Backbone ResNet-18	Breaking the Reclustering Barrier in Centroid-based Deep Clustering 2024-11-04	코드 1
RUC	Accuracy 0.903 Backbone ResNet-18	Improving Unsupervised Image Clustering With Robust Learning 2020-12-21	코드 1
IMC-SwAV (Best)	Accuracy 0.897 NMI 0.818 Train set Train ARI 0.8 Backbone ResNet-18	Information Maximization Clustering via Multi-View Self-Labelling 2021-03-12	코드 1
IMC-SwAV (Avg+-)	Accuracy 0.891 NMI 0.811 Train set Train ARI 0.79 Backbone ResNet-18	Information Maximization Clustering via Multi-View Self-Labelling 2021-03-12	코드 1
TCL	Accuracy 0.887 NMI 0.819 Train set Train ARI 0.780 Backbone ResNet-34	Twin Contrastive Learning for Online Clustering 2022-10-21	코드 1 · 코드 2
HUME	Accuracy 0.884 Train set Train ARI 0.776 Backbone ResNet-18	연결 없음 논문 날짜 미기재	연결 없음
SCAN	Accuracy 0.883 NMI 0.797 Train set Train ARI 0.772 Backbone ResNet-18	SCAN: Learning to Classify Images without Labels 2020-05-25	코드 1 · 코드 2
SCAN (Avg)	Accuracy 0.876 NMI 0.787 Train set Train ARI 0.758 Backbone ResNet-18	SCAN: Learning to Classify Images without Labels 2020-05-25	코드 1 · 코드 2
CoKe	Accuracy 0.857 NMI 0.766 Train set Train ARI 0.732 Backbone ResNet-18	Unsupervised Visual Representation Learning by Online Constrained K-Means 2021-05-24	코드 1
Single-Noun Prior	Accuracy 0.853 NMI 0.731 Train set Train+Test ARI 0.702 Backbone ViT-B-32	Dataset Summarization by K Principal Concepts 2021-04-08	연결 없음
ConCURL	Accuracy 0.846 NMI 0.762 Train set Train ARI 0.715	Representation Learning for Clustering via Building Consensus 2021-05-04	코드 1
ITAE	Accuracy 0.8449 NMI 0.8682 Train set Test ARI 0.7946 Backbone ViT-B/14	Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering 2024-10-07	연결 없음
CoHiClust	Accuracy 0.839 NMI 0.779 Train set Train ARI 0.731 Backbone ResNet-50	Contrastive Hierarchical Clustering 2023-03-03	코드 1
C3	Accuracy 0.838 NMI 0.748 ARI 0.707	C3: Cross-instance guided Contrastive Clustering 2022-11-14	코드 1
MMDC	Accuracy 0.820 NMI 0.703 Backbone ResNet18	Multi-Modal Deep Clustering: Unsupervised Partitioning of Images 2019-12-05	코드 1
IDFD	Accuracy 0.815 NMI 0.711 Train set Train+Test ARI 0.663 Backbone ResNet-18	Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation 2021-05-31	코드 1
TSUC	Accuracy 0.81 NMI - Train set Train ARI - Backbone ResNet-18	Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification 논문 날짜 미기재	코드 1
CC	Accuracy 0.79 NMI 0.705 Train set Train+Test ARI 0.637 Backbone ResNet34	Contrastive Clustering 2020-09-21	코드 1 · 코드 2
DHOG	Accuracy 0.666 NMI 0.585 Train set Train+Test ARI 0.492 Backbone ResNet-18	DHOG: Deep Hierarchical Object Grouping 2020-03-13	연결 없음