FOZO: Forward-Only Zeroth-Order Prompt Optimization for Test-Time Adaptation

TL;DR

FOZO introduces a backpropagation-free, zeroth-order prompt optimization method for test-time adaptation, enabling efficient and stable model updates on resource-constrained devices with superior performance on various ImageNet benchmarks.

Contribution

It proposes FOZO, a novel zeroth-order optimization approach for TTA that avoids backpropagation, improves adaptation stability, and demonstrates superior results on multiple datasets.

Findings

Achieves 59.52% Top-1 accuracy on ImageNet-C

Outperforms gradient-based and SOTA forward-only methods

Generalizes well to quantized models

Abstract

Test-Time Adaptation (TTA) is essential for enabling deep learning models to handle real-world data distribution shifts. However, current approaches face significant limitations: backpropagation-based methods are not suitable for low-end deployment devices, due to their high computation and memory requirements, as well as their tendency to modify model weights during adaptation; while traditional backpropagation-free techniques exhibit constrained adaptation capabilities. In this work, we propose Forward-Only Zeroth-Order Optimization (FOZO), a novel and practical backpropagation-free paradigm for TTA. FOZO leverages a memory-efficient zeroth-order prompt optimization, which is led by objectives optimizing both intermediate feature statistics and prediction entropy. To ensure efficient and stable adaptation over the out-of-distribution data stream, we introduce a dynamically decaying perturbation scale during zeroth-order gradient estimation and theoretically prove its convergence under the TTA data stream assumption. Extensive continual adaptation experiments on ImageNet-C, ImageNet-R, and ImageNet-Sketch demonstrate FOZO's superior performance, achieving 59.52% Top-1 accuracy on ImageNet-C (5K, level 5) and outperforming main gradient-based methods and SOTA forward-only FOA (58.13%). Furthermore, FOZO exhibits strong generalization on quantized (INT8) models. These findings demonstrate that FOZO is a highly competitive solution for TTA deployment in resource-limited scenarios.