What are the current trends and developments in subspace clustering applications in deep learning?
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Subspace clustering in deep learning has seen several trends and developments. One trend is the use of non-convex low-rank models, such as the non-convex Schatten-p norm, which can handle images that do not meet the linear subspace assumption . Another trend is the incorporation of self-supervised learning and contrastive learning techniques to improve the performance of deep subspace clustering algorithms . Additionally, there is a focus on utilizing the structural information in the self-expressive coefficient matrix to enhance clustering performance . Furthermore, there is a shift towards developing deep clustering methods that have linear time and space complexities, making them applicable to large-scale and real-time clustering tasks . These trends and developments aim to enhance the accuracy, scalability, and efficiency of subspace clustering applications in deep learning.
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| Papers (4) | Insight |
|---|---|
| The provided paper proposes a double self-expressive subspace clustering algorithm that utilizes the self-expressive coefficient matrix to improve clustering performance. It also introduces a self-supervised module based on contrastive learning to enhance the trained network's performance. | |
| The provided paper proposes a double self-expressive subspace clustering algorithm that utilizes the self-expressive coefficient matrix to improve clustering performance. It also introduces a self-supervised module based on contrastive learning to enhance the trained network's performance. | |
| The provided paper introduces a novel algorithm called S^3CE, which combines self-supervised contrastive learning and entropy-norm constraint to improve the performance of deep subspace clustering. | |
| The provided paper proposes a non-convex low-rank subspace clustering method that involves deep learning and introduces convolutional self-encoding and self-representation layers to extract non-linear features of images. It also uses the non-convex Schatten-p norm to characterize matrix rank. The paper does not specifically discuss current trends and developments in subspace clustering applications in deep learning. |
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