# Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

arXiv: https://arxiv.org/abs/1506.02565 · HTML: https://mlsanity.com/paper/1506.02565/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2015.

## Built on by (most cited first, 3 of 3)

- [Which Model to Transfer? A Survey on Transferability Estimation](https://mlsanity.com/paper/2402.15231/) (2024)
- [Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance](https://mlsanity.com/paper/2605.01325/) (2026)
- [Pre-Trained Model Recommendation for Downstream Fine-tuning](https://mlsanity.com/paper/2403.06382/) (2024)

## Abstract (arXiv)

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning; the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.