Sparse Token

Sparse token methods aim to improve the efficiency and performance of deep learning models, particularly transformers, by focusing computation on the most informative parts of input data. Current research explores various techniques, including learnable meta-tokens, attention mechanisms that selectively process tokens, and algorithms for efficient token pruning and reconstruction, often within Mixture-of-Experts (MoE) frameworks. This focus on sparsity offers significant advantages in reducing computational cost and improving model speed, with applications ranging from image segmentation and visual question answering to large-scale language modeling and time-series analysis. The resulting efficiency gains are particularly impactful for resource-constrained environments and real-time applications.