Research map / Label-efficient and robust learning / Recognition and long-tail
Unsupervised Learning of Group Invariant and Equivariant Representations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use group-invariant embedding instead of non-invariant autoencoder embedding | autoencoder · ShapeNet KNN shape classification | accuracy: 0.63 -> 0.81 | Helped |
| use permutation-invariant autoencoder instead of classical autoencoder | autoencoder · Sets of digits (N=20 to 100) | embedding size needed for perfect reconstruction | Helped |
| use SO(2)-invariant autoencoder instead of classical autoencoder | autoencoder · Rotated MNIST latent clustering | cluster structure (qualitative) | Helped |
Closest papers by meaning
- Lie Group Decompositions for Equivariant Neural Networks (ICLR 2024 · similarity 0.91)
- Equivariance by Contrast: Identifiable Equivariant Embeddings from Unlabeled Finite Group Actions (NeurIPS 2025 · similarity 0.91)
- A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models (NeurIPS 2025 · similarity 0.89)
- Neural Fourier Transform: A General Approach to Equivariant Representation Learning (ICLR 2024 · similarity 0.89)
- Affine Equivariant Networks Based on Differential Invariants (CVPR 2024 · similarity 0.89)
- Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts (ICML 2024 · similarity 0.89)
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