Papers › Let Go of Your Labels with Unsupervised Transfer

Let Go of Your Labels with Unsupervised Transfer

11 Jun 2024International Conference on Machine Learning 2024 6arXiv:2406.07236archive 2025-07-28

Artyom Gadetsky, Yulun Jiang, Maria Brbic

Foundation vision-language models have enabled remarkable zero-shot transferability of the pre-trained representations to a wide range of downstream tasks. However, to solve a new task, zero-shot transfer still necessitates human guidance to define visual categories that appear in the data. Here, we show that fully unsupervised transfer emerges when searching for the labeling of a dataset that induces maximal margin classifiers in representation spaces of different foundation models. We present TURTLE, a fully unsupervised method that effectively employs this guiding principle to uncover the underlying labeling of a downstream dataset without any supervision and task-specific representation learning. We evaluate TURTLE on a diverse benchmark suite of 26 datasets and show that it achieves new state-of-the-art unsupervised performance. Furthermore, TURTLE, although being fully unsupervised, outperforms zero-shot transfer baselines on a wide range of datasets. In particular, TURTLE matches the average performance of CLIP zero-shot on 26 datasets by employing the same representation space, spanning a wide range of architectures and model sizes. By guiding the search for the underlying labeling using the representation spaces of two foundation models, TURTLE surpasses zero-shot transfer and unsupervised prompt tuning baselines, demonstrating the surprising power and effectiveness of unsupervised transfer.

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Tasks

Image ClusteringUnsupervised Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Clustering Birdsnap TURTLE (CLIP + DINOv2) Accuracy 68.1 #1 of 1 Archive leaderboard report
Image Clustering CIFAR-10 TURTLE (CLIP + DINOv2) ARI 0.989 #1 of 40 Archive leaderboard report
Image Clustering CIFAR-10 TURTLE (CLIP + DINOv2) Accuracy 0.995 #1 of 40 Archive leaderboard report
Image Clustering CIFAR-10 TURTLE (CLIP + DINOv2) NMI 0.985 #1 of 40 Archive leaderboard report
Image Clustering CIFAR-100 TURTLE (CLIP + DINOv2) ARI 0.834 #1 of 30 Archive leaderboard report
Image Clustering CIFAR-100 TURTLE (CLIP + DINOv2) Accuracy 0.898 #1 of 30 Archive leaderboard report
Image Clustering CIFAR-100 TURTLE (CLIP + DINOv2) NMI 0.915 #1 of 30 Archive leaderboard report
Image Clustering CLEVR Counts TURTLE (CLIP + DINOv2) Accuracy 24.0 #1 of 1 Archive leaderboard report
Image Clustering Caltech-101 TURTLE (CLIP + DINOv2) Accuracy 89.8 #1 of 1 Archive leaderboard report
Image Clustering Country211 TURTLE (CLIP + DINOv2) Accuracy 11.1 #1 of 1 Archive leaderboard report
Image Clustering DTD TURTLE (CLIP + DINOv2) Accuracy 57.3 #2 of 2 Archive leaderboard report
Image Clustering EuroSAT TURTLE (CLIP + DINOv2) Accuracy 96.6 #1 of 1 Archive leaderboard report
Image Clustering FER2013 TURTLE (CLIP + DINOv2) Accuracy 36.2 #1 of 1 Archive leaderboard report
Image Clustering FGVC Aircraft TURTLE (CLIP + DINOv2) Accuracy 36.5 #1 of 1 Archive leaderboard report
Image Clustering Flowers-102 TURTLE (CLIP + DINOv2) Accuracy 99.6 #1 of 1 Archive leaderboard report
Image Clustering Food-101 TURTLE (CLIP + DINOv2) Accuracy 92.2 #1 of 1 Archive leaderboard report
Image Clustering GTSRB TURTLE (CLIP + DINOv2) Accuracy 48.4 #1 of 1 Archive leaderboard report
Image Clustering Hateful Memes TURTLE (CLIP + DINOv2) Accuracy 54.2 #1 of 1 Archive leaderboard report
Image Clustering ImageNet TURTLE (CLIP + DINOv2) ARI 62.5 #1 of 12 Archive leaderboard report
Image Clustering ImageNet TURTLE (CLIP + DINOv2) Accuracy 72.9 #1 of 12 Archive leaderboard report
Image Clustering ImageNet TURTLE (CLIP + DINOv2) NMI 88.2 #1 of 12 Archive leaderboard report
Image Clustering KITTI TURTLE (CLIP + DINOv2) Accuracy 39.4 #1 of 1 Archive leaderboard report
Image Clustering Kinetics-700 TURTLE (CLIP + DINOv2) Accuracy 43.0 #1 of 1 Archive leaderboard report
Image Clustering MNIST TURTLE (CLIP + DINOv2) Accuracy 97.8 #1 of 3 Archive leaderboard report
Image Clustering Oxford-IIIT Pets TURTLE (CLIP + DINOv2) Accuracy 92.3 #1 of 1 Archive leaderboard report
Image Clustering PCam TURTLE (CLIP + DINOv2) Accuracy 52.0 #1 of 1 Archive leaderboard report
Image Clustering RESISC45 TURTLE (CLIP + DINOv2) Accuracy 89.6 #1 of 1 Archive leaderboard report
Image Clustering Rendered SST2 TURTLE (CLIP + DINOv2) Accuracy 51.6 #1 of 1 Archive leaderboard report
Image Clustering STL-10 TURTLE (CLIP + DINOv2) ARI 0.994 #1 of 29 Archive leaderboard report
Image Clustering STL-10 TURTLE (CLIP + DINOv2) Accuracy 0.997 #1 of 29 Archive leaderboard report
Image Clustering STL-10 TURTLE (CLIP + DINOv2) NMI 0.993 #1 of 29 Archive leaderboard report
Image Clustering SUN397 TURTLE (CLIP + DINOv2) Accuracy 67.9 #1 of 1 Archive leaderboard report
Image Clustering Stanford Cars TURTLE (CLIP + DINOv2) Accuracy 0.646 #1 of 5 Archive leaderboard report
Image Clustering UCF101 TURTLE (CLIP + DINOv2) Accuracy 82.3 #1 of 2 Archive leaderboard report
Unsupervised Image Classification CIFAR-10 TURTLE (CLIP + DINOv2) Accuracy 99.5 #1 of 9 Archive leaderboard report
Unsupervised Image Classification ImageNet TURTLE (CLIP + DINOv2) ARI 62.5 #1 of 9 Archive leaderboard report
Unsupervised Image Classification ImageNet TURTLE (CLIP + DINOv2) Accuracy (%) 72.9 #1 of 9 Archive leaderboard report
Unsupervised Image Classification MNIST TURTLE (CLIP + DINOv2) Accuracy 97.8 #3 of 10 Archive leaderboard report
Unsupervised Image Classification STL-10 TURTLE (CLIP + DINOv2) Accuracy 99.7 #1 of 9 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CLIP

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