More Than A Shortcut: A Hyperbolic Approach To Early-Exit Networks

2025-11-01

AI Summary

Existing Early-Exit (EE) networks for efficient event detection on resource-constrained devices suffer from Euclidean space limitations, hindering hierarchical consistency and reliability of early predictions. Method: We propose HypEE, a hyperbolic early-exit network that constructs hierarchical representations with partial-order constraints in hyperbolic space. It introduces an entailment loss for geometry-aware hierarchical training and equips the model with intrinsic uncertainty quantification to enable more reliable dynamic exit decisions. Contribution/Results: Compared to Euclidean EE baselines, HypEE significantly improves accuracy at the earliest exit layer across multiple audio event detection tasks, while also enhancing overall accuracy and inference efficiency. HypEE is the first framework to deeply integrate hyperbolic geometric modeling with early-exit mechanisms, establishing a novel paradigm for lightweight temporal understanding.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Mixture of Experts (MoE)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Deploying accurate event detection on resource-constrained devices is challenged by the trade-off between performance and computational cost. While Early-Exit (EE) networks offer a solution through adaptive computation, they often fail to enforce a coherent hierarchical structure, limiting the reliability of their early predictions. To address this, we propose Hyperbolic Early-Exit networks (HypEE), a novel framework that learns EE representations in the hyperbolic space. Our core contribution is a hierarchical training objective with a novel entailment loss, which enforces a partial-ordering constraint to ensure that deeper network layers geometrically refine the representations of shallower ones. Experiments on multiple audio event detection tasks and backbone architectures show that HypEE significantly outperforms standard Euclidean EE baselines, especially at the earliest, most computationally-critical exits. The learned geometry also provides a principled measure of uncertainty, enabling a novel triggering mechanism that makes the overall system both more efficient and more accurate than a conventional EE and standard backbone models without early-exits.

Problem

• Balancing performance and computational cost for event detection on resource-constrained devices
• Enforcing coherent hierarchical structure in early-exit networks for reliable predictions
• Learning hyperbolic representations to geometrically refine shallow layer outputs

Innovation

• Hyperbolic space learning for early-exit networks
• Hierarchical training with novel entailment loss
• Geometric refinement of representations across layers