2020/06/11 by Shuai Tang, Tang, Shuai, Virginia R. de +2
Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2006.06791
arxiv created 2020/06/11 · openalex publication_date 2020/06/11 · arxiv updated 2020/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The large amount of online data and vast array of computing resources enable current researchers in both industry and academia to employ the power of deep learning with neural networks. While deep models trained with massive amounts of data demonstrate promising generalisation ability on unseen data from relevant domains, the computational cost of finetuning gradually becomes a bottleneck in transfering the learning to new domains. We address this issue by leveraging the low-rank property of learnt feature vectors produced from deep neural networks (DNNs) with the closed-form solution provided in kernel ridge regression (KRR). This frees transfer learning from finetuning and replaces it with an ensemble of linear systems with many fewer hyperparameters. Our method is successful on supervised and semi-supervised transfer learning tasks.