Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels.
Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020)
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In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Ranked #4 on Image Clustering on Tiny-ImageNet
First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods.
Ranked #6 on Image Clustering on Imagenet-dog-15
Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).
First, a self-supervised task from representation learning is employed to obtain semantically meaningful features.
Ranked #1 on Image Clustering on ImageNet
Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
Ranked #2 on Image Clustering on ImageNet
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.
Ranked #1 on Image Generation on CUB 128 x 128
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.
Ranked #3 on Unsupervised Image Classification on SVHN (using extra training data)
In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
Ranked #4 on Motion Segmentation on Hopkins155