Fractal-Guided Token Pruning for Efficient Vision Transformers

Vision Transformers achieve strong performance across computer vision tasks but suffer from quadratic computational complexity with respect to token count, limiting deployment in resource-constrained environments. Existing token pruning methods rely on attention scores to identify important tokens, but attention mechanisms capture query-specific relevance rather than intrinsic information content, potentially discarding tokens that carry information for subsequent layers or different downstream tasks. We propose fractal-guided token pruning, a method that leverages the correlation dimension Dcorr of token embeddings as a task-agnostic measure of geometric complexity. Our key insight is that tokens with high Dcorr span higher-dimensional manifolds in representation space, indicating complex patterns, while tokens with low Dcorr collapse to simpler structures representing redundant information. By computing a local Dcorr for each token and pruning those with the lowest values, our method retains geometrically complex tokens independent of attention-based relevance. The correlation dimension quantifies how token embeddings fill the representation space: embeddings from uniform background regions cluster tightly in low-dimensional subspaces (low Dcorr), while embeddings from complex textures or object boundaries spread across higher-dimensional manifolds (high Dcorr), reflecting their richer information content. Experiments on CIFAR-10 and CIFAR-100 with fine-tuned ViT-B/16 models show that fractal-guided pruning consistently outperforms random and norm-based pruning across all tested ratios. At forty percent pruning, fractal pruning maintains 92.26% accuracy on CIFAR-10 with only a 0.99 percentage point drop from the 93.25% baseline while achieving 1.17× speedup. Our approach provides a geometry-based criterion for token importance that complements attention-based methods and shows promising generalization between CIFAR-10 and CIFAR-100 datasets.