Transferability Measurement Algorithm

A Transferability Measurement Algorithm is an Model Evaluation Algorithm that estimates how effectively a model or representation trained on a source task can be reused or fine-tuned for a target task, without requiring extensive retraining or evaluation on the target task.

• AKA: Transferability Estimation Algorithm, Transfer Scoring Function, Task Transferability Metric.
• Context:
  • It can be implemented by an Transferability Measurement System to systematically solve Transferability Measurement Tasks.
  • It can be used to evaluate the suitability of a pre-trained model or dataset for a downstream target task in the context of transfer learning.
  • It can guide the selection of source models or datasets in low-resource settings, especially when labeled data for the target task is limited or unavailable.
  • It can reduce computational cost by eliminating the need to train full models on all potential target tasks.
  • It can be applied across domains such as computer vision, natural language processing, and speech recognition, wherever source-to-target model transfer is common.
  • It can support model selection, source task ranking, dataset curation, and task-to-task recommendation.
  • It can complement benchmarking protocols by providing pre-transfer diagnostic signals.
  • It can use techniques such as:
    • Log Expected Empirical Prediction (LEEP), to compute transferability using likelihood-based assumptions.
    • Negative Conditional Entropy (NCE), to assess mutual dependence between learned features and target labels.
    • TransRate, to evaluate mutual information between features and task labels.
    • TMI, to evaluate intra-class feature variance as an indicator of generalization potential.
  • It can produce quantitative scores that are used as transferability measurement metrics, which are numerical indicators of source-to-target task compatibility.
  • It can be considered an algorithm-type concept because it systematically computes a transferability score from features and labels.
  • It can also support measurement-type or metric-type roles when the focus is on the transferability score itself (e.g., LEEP, NCE, or TransRate).
  • ...
• Example(s):
  • LEEP Algorithm, which measures how well a model trained on one task transfers to another by evaluating log-likelihood estimates.
  • TMI Algorithm, which measures intra-class variance in feature space to infer generalization potential.
  • TransRate, which uses mutual information between task features and target labels to compute a transferability score.
  • H-Score, which evaluates the correlation between task performance and domain similarity.
  • ...
• Counter-Example(s):
  • Model Evaluation Algorithm, which assesses model performance on a fully trained model, not its pre-transfer utility.
  • Hyperparameter Optimization Algorithm, which tunes model parameters but does not assess transferability between tasks.
  • Adversarial Learning Algorithm, which increases robustness against input perturbations but is not concerned with source-target task alignment.
  • ...
• See: Transfer Learning, Task Transferability Prediction, Log Expected Empirical Prediction (LEEP), Negative Conditional Entropy, TMI, TransRate, Source-Target Suitability Estimation.

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References

2023

• (Xu et al., 2023) ⇒ Zheyang Xu, Haohe Liu, Xinhao Mei, Yiming Hu, & Wangmeng Xiang. (2023). "Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance". In: Proceedings of ICCV 2023.
  • QUOTE: Proposes a transferability measurement method based on intra-class feature variance analysis, achieving 98% correlation with ground-truth transfer accuracy on ImageNet-to-CIFAR benchmarks. The approach requires only 0.02 seconds per measurement and demonstrates label-free capability through self-supervised feature clustering.

2022

• (Huang et al., 2022) ⇒ Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, & Ying Wei. (2022). "Frustratingly Easy Transferability Estimation". In: Proceedings of ICML 2022.
  • QUOTE: Introduces TransRate, a transferability measurement that computes mutual information between pre-trained features and target labels via coding rate estimation. Evaluates 32 models across 16 tasks with 10-line code implementation, showing 0.92 Spearman correlation with fine-tuning results while requiring only single forward passes.

2021

• (Tran et al., 2021) ⇒ Anh Tuan Tran, Cuong V. Nguyen, & Tal Hassner. (2021). "Transferability and Hardness of Supervised Classification Tasks". arXiv Preprint.
  • QUOTE: Develops Negative Conditional Entropy (NCE) as transferability measurement, establishing theoretical connections between task similarity and transfer learning performance. Validated on 15 image datasets with 0.85 Kendall-τ correlation between predicted and actual transfer accuracy across different label space mismatch scenarios

2020

• (Nguyen et al., 2020) ⇒ Cuong V. Nguyen, Tal Hassner, Matthias Seeger, & Cedric Archambeau. (2020). "LEEP: A New Measure to Evaluate Transferability of Learned Representations". In: Proceedings of ICML 2020.
  • QUOTE: Proposes Log Expected Empirical Prediction (LEEP) for transferability measurement, requiring only single forward pass through source model. Demonstrates 30% improvement over prior methods in ImageNet-to-CIFAR100 transfer prediction accuracy while maintaining linear time complexity relative to dataset size.

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