Transferability
5 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Ranking Neural Checkpoints
This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task.
LogME: Practical Assessment of Pre-trained Models for Transfer Learning
In pursuit of a practical assessment method, we propose to estimate the maximum value of label evidence given features extracted by pre-trained models.
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks
With the increasing abundance of pretrained models in recent years, the problem of selecting the best pretrained checkpoint for a particular downstream classification task has been gaining increased attention.
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space
It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive.
ETran: Energy-Based Transferability Estimation
This is the first work that proposes transferability estimation for object detection task.