SURGE: Surrogate Gradient Adaptation in Binary Neural Networks

• URL: http://arxiv.org/abs/2605.10989v2
• Date: Fri, 15 May 2026 04:30:07 GMT
• Title: SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
• Abstract summary: We propose SURrogate GradiEnt Adaptation (SURGE), a learnable gradient compensation framework with theoretical grounding.<n> SURGE mitigates gradient mismatch through auxiliary backpropagation.<n> Experiments on image classification, object detection, and language understanding tasks demonstrate that SURGE performs best over state-of-the-art methods.
• Score: 41.424349870612716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sign function). However, prevailing methods including the Straight-Through Estimator (STE) and its improved variants, rely on hand-crafted designs that suffer from gradient mismatch problem and information loss induced by fixed-range gradient clipping. To address this, we propose SURrogate GradiEnt Adaptation (SURGE), a novel learnable gradient compensation framework with theoretical grounding. SURGE mitigates gradient mismatch through auxiliary backpropagation. Specifically, we design a Dual-Path Gradient Compensator (DPGC) that constructs a parallel full-precision auxiliary branch for each binarized layer, decoupling gradient flow via output decomposition during backpropagation. DPGC enables bias-reduced gradient estimation by leveraging the full-precision branch to estimate components beyond STE's first-order approximation. To further enhance training stability, we introduce an Adaptive Gradient Scaler (AGS) based on an optimal scale factor to dynamically balance inter-branch gradient contributions via norm-based scaling. Experiments on image classification, object detection, and language understanding tasks demonstrate that SURGE performs best over state-of-the-art methods.

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