HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning

• URL: http://arxiv.org/abs/2504.15323v1
• Date: Mon, 21 Apr 2025 03:04:38 GMT
• Title: HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning
• Authors: Donggyun Kim, Chanwoo Kim, Seunghoon Hong,
• Abstract summary: We propose an approach that emulates gradient descent without computing gradients, enabling efficient test-time adaptation.<n>Specifically, we formulate gradient descent as an Euler discretization of an ordinary differential equation (ODE) and train an auxiliary network to predict the task-conditional drift.<n>The adaptation then reduces to a simple numerical integration, which requires only a few forward passes of the auxiliary network.
• Score: 20.308785668386424
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While test-time fine-tuning is beneficial in few-shot learning, the need for multiple backpropagation steps can be prohibitively expensive in real-time or low-resource scenarios. To address this limitation, we propose an approach that emulates gradient descent without computing gradients, enabling efficient test-time adaptation. Specifically, we formulate gradient descent as an Euler discretization of an ordinary differential equation (ODE) and train an auxiliary network to predict the task-conditional drift using only the few-shot support set. The adaptation then reduces to a simple numerical integration (e.g., via the Euler method), which requires only a few forward passes of the auxiliary network -- no gradients or forward passes of the target model are needed. In experiments on cross-domain few-shot classification using the Meta-Dataset and CDFSL benchmarks, our method significantly improves out-of-domain performance over the non-fine-tuned baseline while incurring only 6\% of the memory cost and 0.02\% of the computation time of standard fine-tuning, thus establishing a practical middle ground between direct transfer and fully fine-tuned approaches.

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