</> Code with Papers by Syntology

Papers › Deep Adaptive Image Clustering

Deep Adaptive Image Clustering

1 Oct 2017ICCV 2017 10archive 2025-07-28

Jianlong Chang, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Image clustering is a crucial but challenging task in machine learning and computer vision. Existing methods often ignore the combination between feature learning and clustering. To tackle this problem, we propose Deep Adaptive Clustering (DAC) that recasts the clustering problem into a binary pairwise-classification framework to judge whether pairs of images belong to the same clusters. In DAC, the similarities are calculated as the cosine distance between label features of images which are generated by a deep convolutional network (ConvNet). By introducing a constraint into DAC, the learned label features tend to be one-hot vectors that can be utilized for clustering images. The main challenge is that the ground-truth similarities are unknown in image clustering. We handle this issue by presenting an alternating iterative Adaptive Learning algorithm where each iteration alternately selects labeled samples and trains the ConvNet. Conclusively, images are automatically clustered based on the label features. Experimental results show that DAC achieves state-of-the-art performance on five popular datasets, e.g., yielding 97.75% clustering accuracy on MNIST, 52.18% on CIFAR-10 and 46.99% on STL-10.

PaperPDFCode

Code

vector-1127/DAC officialmentioned in paper report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

ClusteringImage Clustering

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Clustering CIFAR-10 DAC ARI 0.301 #33 of 40 Archive leaderboard report
Image Clustering CIFAR-10 DAC Accuracy 0.522 #33 of 40 Archive leaderboard report
Image Clustering CIFAR-10 DAC Backbone ConvNet #33 of 40 Archive leaderboard report
Image Clustering CIFAR-10 DAC NMI 0.4 #33 of 40 Archive leaderboard report
Image Clustering CIFAR-10 DAC Train set Train+Test #33 of 40 Archive leaderboard report
Image Clustering CIFAR-100 DAC Accuracy 0.238 #24 of 30 Archive leaderboard report
Image Clustering CIFAR-100 DAC NMI 0.185 #24 of 30 Archive leaderboard report
Image Clustering CIFAR-100 DAC Train Set Train+Test #24 of 30 Archive leaderboard report
Image Clustering ImageNet-10 DAC Accuracy 0.527 #14 of 18 Archive leaderboard report
Image Clustering ImageNet-10 DAC NMI 0.394 #14 of 18 Archive leaderboard report
Image Clustering Imagenet-dog-15 DAC Accuracy 0.275 #16 of 20 Archive leaderboard report
Image Clustering Imagenet-dog-15 DAC NMI 0.219 #16 of 20 Archive leaderboard report
Image Clustering STL-10 DAC Accuracy 0.470 #25 of 29 Archive leaderboard report
Image Clustering STL-10 DAC Backbone ConvNet #25 of 29 Archive leaderboard report
Image Clustering STL-10 DAC NMI 0.366 #25 of 29 Archive leaderboard report
Image Clustering STL-10 DAC Train Split Train+Test #25 of 29 Archive leaderboard report
Image Clustering Tiny-ImageNet DAC Accuracy 0.066 #10 of 14 Archive leaderboard report
Image Clustering Tiny-ImageNet DAC NMI 0.190 #10 of 14 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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections