# LEEP: A New Measure to Evaluate Transferability of Learned Representations

arXiv: https://arxiv.org/abs/2002.12462 · HTML: https://mlsanity.com/paper/2002.12462/

## Key facts

- Cited by 188 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 30 of 188)

- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [Deep Model Reassembly](https://mlsanity.com/paper/2210.17409/) (2022)
- [Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2303.08566/) (2023)
- [What to Pre-Train on? Efficient Intermediate Task Selection](https://mlsanity.com/paper/2104.08247/) (2021)
- [OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations](https://mlsanity.com/paper/2103.13843/) (2021)
- [Transferability in Deep Learning: A Survey](https://mlsanity.com/paper/2201.05867/) (2022)
- [Transferability Estimation using Bhattacharyya Class Separability](https://mlsanity.com/paper/2111.12780/) (2021)
- [Scaling Laws for Downstream Task Performance of Large Language Models](https://mlsanity.com/paper/2402.04177/) (2024)
- [Frustratingly Easy Transferability Estimation](https://mlsanity.com/paper/2106.09362/) (2021)
- [Memory Efficient Continual Learning with Transformers](https://mlsanity.com/paper/2203.04640/) (2022)
- [A linearized framework and a new benchmark for model selection for fine-tuning](https://mlsanity.com/paper/2102.00084/) (2021)
- [Scalable Diverse Model Selection for Accessible Transfer Learning](https://mlsanity.com/paper/2111.06977/) (2021)
- [Zoo-Tuning: Adaptive Transfer from a Zoo of Models](https://mlsanity.com/paper/2106.15434/) (2021)
- [Selecting Large Language Model to Fine-tune via Rectified Scaling Law](https://mlsanity.com/paper/2402.02314/) (2024)
- [Model Spider: Learning to Rank Pre-Trained Models Efficiently](https://mlsanity.com/paper/2306.03900/) (2023)
- [PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks](https://mlsanity.com/paper/2203.05126/) (2022)
- [How stable are Transferability Metrics evaluations?](https://mlsanity.com/paper/2204.01403/) (2022)
- [Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space](https://mlsanity.com/paper/2207.03036/) (2022)
- [Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs](https://mlsanity.com/paper/2110.10545/) (2021)
- [Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey](https://mlsanity.com/paper/2305.05080/) (2023)
- [Wasserstein Task Embedding for Measuring Task Similarities](https://mlsanity.com/paper/2208.11726/) (2022)
- [Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing](https://mlsanity.com/paper/2502.17282/) (2025)
- [We Should Chart an Atlas of All the World's Models](https://mlsanity.com/paper/2503.10633/) (2025)
- [Transferability Metrics for Selecting Source Model Ensembles](https://mlsanity.com/paper/2111.13011/) (2021)
- [Wings: Learning Multimodal LLMs without Text-only Forgetting](https://mlsanity.com/paper/2406.03496/) (2024)
- [Identification of Negative Transfers in Multitask Learning Using Surrogate Models](https://mlsanity.com/paper/2303.14582/) (2023)
- [How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?](https://mlsanity.com/paper/2501.11253/) (2025)
- [A Multiplexed Network for End-to-End, Multilingual OCR](https://mlsanity.com/paper/2103.15992/) (2021)
- [Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How](https://mlsanity.com/paper/2306.03828/) (2023)
- [ETran: Energy-Based Transferability Estimation](https://mlsanity.com/paper/2308.02027/) (2023)

## Abstract (arXiv)

We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy to compute: when given a classifier trained on a source data set, it only requires running the target data set through this classifier once. We analyze the properties of LEEP theoretically and demonstrate its effectiveness empirically. Our analysis shows that LEEP can predict the performance and convergence speed of both transfer and meta-transfer learning methods, even for small or imbalanced data. Moreover, LEEP outperforms recently proposed transferability measures such as negative conditional entropy and H scores. Notably, when transferring from ImageNet to CIFAR100, LEEP can achieve up to 30% improvement compared to the best competing method in terms of the correlations with actual transfer accuracy.