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Related Experiment Video

Updated: Jun 22, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Hydra: Multi-head low-rank adaptation for parameter efficient fine-tuning.

Sanghyeon Kim¹, Hyunmo Yang², Yunghyun Kim²

¹Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066, Seoubu-ro, Suwon, 16419, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society

|June 27, 2024

Summary

This summary is machine-generated.

Hydra enhances foundation model adaptation by combining parallel and sequential adapter branches. This novel approach improves parameter efficiency and generalization for diverse downstream tasks, outperforming existing methods.

Keywords:

Adapter Parameter efficient fine-tuning Transformer

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Large-scale foundation models require efficient adaptation techniques for downstream tasks.
Low-rank adaptation (LoRA) methods offer parameter efficiency and no inference latency.

Purpose of the Study:

To introduce Hydra, a more general adapter module for foundation models.
To integrate parallel and sequential adaptation branches for enhanced expressiveness and fine-tuning exploration.

Main Methods:

Hydra combines parallel and sequential adaptation branches.
It leverages pre-trained weights via linear combination of features.
Comprehensive analysis of adaptation branch characteristics was performed.

Main Results:

Hydra demonstrates superior performance and efficiency in extensive experiments.
The integrated approach allows exploration of broader optimal fine-tuning points.
Learned features show improved generalization across diverse downstream tasks.

Conclusions:

Hydra offers a more expressive and effective method for adapting foundation models.
The combined branch strategy enhances generalization and fine-tuning capabilities.
This approach has significant potential for various AI applications.