Papers › DLME: Deep Local-flatness Manifold Embedding
DLME: Deep Local-flatness Manifold Embedding
Zelin Zang, Siyuan Li, Di wu, Ge Wang, Lei Shang, Baigui Sun, Hao Li, Stan Z. Li
Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, and we find that previous methods perform poorly in this case. Generally, ML methods first transform input data into a low-dimensional embedding space to maintain the data's geometric structure and subsequently perform downstream tasks therein. The poor local connectivity of under-sampling data in the former step and inappropriate optimization objectives in the latter step leads to two problems: structural distortion and underconstrained embedding. This paper proposes a novel ML framework named Deep Local-flatness Manifold Embedding (DLME) to solve these problems. The proposed DLME constructs semantic manifolds by data augmentation and overcomes the structural distortion problem using a smoothness constrained based on a local flatness assumption about the manifold. To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness. Experiments on three types of datasets (toy, biological, and image) for various downstream tasks (classification, clustering, and visualization) show that our proposed DLME outperforms state-of-the-art ML and contrastive learning methods.
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57bf8a530a89bd9c · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | DLME (ResNet-18, linear) | Percentage correct | 91.3 | #193 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | DLME (ResNet-18, linear) | Percentage correct | 66.1 | #190 of 211 | Archive leaderboard | report |
| Image Classification | ImageNet-100 (TEMI Split) | DLME (ResNet-50, linear) | Percentage correct | 79.3 | #2 of 2 | Archive leaderboard | report |
| Image Classification | STL-10 | DLME (ResNet-50, linear) | Percentage correct | 90.1 | #35 of 117 | Archive leaderboard | report |
| Image Classification | Tiny-ImageNet | DLME (ResNet-18, linear) | Top 1 Accuracy | 44.9 | #3 of 4 | 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
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