Large Convolutional Model Tuning via Filter Subspace
Wei Chen Zichen Miao Qiang Qiu
Efficient fine-tuning methods are critical to address the high computational and parameter complexity while adapting large pre-trained models to downstream tasks.
Our study is inspired by prior research that represents each convolution filter as a linear combination of a small set of filter subspace elements, referred to as filter atoms. In this paper, we propose to fine-tune pre-trained models by adjusting only filter atoms, which are responsible for spatial-only convolution, while preserving spatially-invariant channel combination knowledge in atom coefficients.
In this way, we bring a new filter subspace view for model tuning. 
Furthermore, each filter atom can be recursively decomposed as a combination of another set of atoms, which naturally expands the number of tunable parameters in the filter subspace.
By only adapting filter atoms constructed by a small number of parameters, while maintaining the rest of model parameters constant, the proposed approach is highly parameter-efficient. It effectively preserves the capabilities of pre-trained models and prevents overfitting to downstream tasks. 
Extensive experiments show that such a simple scheme surpasses previous tuning baselines for both discriminate and generative tasks.
Efficient Fine-tuning, Filter Decomposition, Filter Subspace