Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces

arXiv:2609.27988 · cs.CV, cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Project page: https://cyberiada.github.io/TaskInducedViTs

Terminology

• Pruning — 64× in this paper · explained in 4 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Cosine Similarity — 8× in this paper · explained in 3 episodes
• Last-Layer Fusion — 7× in this paper
• Accuracy — 6× in this paper · explained in 1 episode
• Latent Space — 5× in this paper · explained in 7 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Spectral Pullback Network — 4× in this paper
• Distillation — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Spectral Gap — 3× in this paper · explained in 5 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Stochastic Lanczos Quadrature — 2× in this paper
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Amortization — 1× in this paper · explained in 1 episode
• Attention Entropy — 1× in this paper · explained in 2 episodes
• Axioms — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distilled Riemannian Metric — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Distilling the Knowledge in a Neural Network
• Geometry-Aware Hamiltonian Variational Auto-Encoder
• A Review on Riemannian Metric Learning: Closer to You than You Imagine
• SmoothGrad: removing noise by adding noise

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