# ETran: Energy-Based Transferability Estimation

arXiv: https://arxiv.org/abs/2308.02027 · HTML: https://mlsanity.com/paper/2308.02027/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020)
- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [An Information-Theoretic Approach to Transferability in Task Transfer Learning](https://mlsanity.com/paper/2212.10082/) (2022)
- [OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations](https://mlsanity.com/paper/2103.13843/) (2021)
- [Transferability Estimation using Bhattacharyya Class Separability](https://mlsanity.com/paper/2111.12780/) (2021)
- [Ranking Neural Checkpoints](https://mlsanity.com/paper/2011.11200/) (2020)
- [Eigenvalue and Generalized Eigenvalue Problems: Tutorial](https://mlsanity.com/paper/1903.11240/) (2019)
- [PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks](https://mlsanity.com/paper/2203.05126/) (2022)
- [Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space](https://mlsanity.com/paper/2207.03036/) (2022)
- [Fisher and Kernel Fisher Discriminant Analysis: Tutorial](https://mlsanity.com/paper/1906.09436/) (2019)
- [E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models](https://mlsanity.com/paper/2203.00748/) (2022)
- [EBJR: Energy-Based Joint Reasoning for Adaptive Inference](https://mlsanity.com/paper/2110.10343/) (2021)

## Built on by (most cited first, 18 of 18)

- [Foundation Model is Efficient Multimodal Multitask Model Selector](https://mlsanity.com/paper/2308.06262/) (2023)
- [Which Model to Transfer? A Survey on Transferability Estimation](https://mlsanity.com/paper/2402.15231/) (2024)
- [Sequence Transferability and Task Order Selection in Continual Learning](https://mlsanity.com/paper/2502.06544/) (2025)
- [Exploring Structural Degradation in Dense Representations for Self-supervised Learning](https://mlsanity.com/paper/2510.17299/) (2025)
- [LEAD: Exploring Logit Space Evolution for Model Selection](https://mlsanity.com/paper/2507.14559/) (2025)
- [Benchmarking Transferability: A Framework for Fair and Robust Evaluation](https://mlsanity.com/paper/2504.20121/) (2025)
- [Estimating Time Series Foundation Model Transferability via In-Context Learning](https://mlsanity.com/paper/2509.23695/) (2025)
- [How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation](https://mlsanity.com/paper/2510.06448/) (2025)
- [Pre-Trained Model Recommendation for Downstream Fine-tuning](https://mlsanity.com/paper/2403.06382/) (2024)
- [ESCAPE: Energy-based Selective Adaptive Correction for Out-of-distribution 3D Human Pose Estimation](https://mlsanity.com/paper/2407.14605/) (2024)
- [When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective](https://mlsanity.com/paper/2409.01821/) (2024)
- [k-NN as a Simple and Effective Estimator of Transferability](https://mlsanity.com/paper/2503.18528/) (2025)
- [SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning](https://mlsanity.com/paper/2510.23051/) (2025)
- [Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models](https://mlsanity.com/paper/2607.04199/) (2026)
- [Back to the Basics on Predicting Transfer Performance](https://mlsanity.com/paper/2405.20420/) (2024)
- [Implicit Modeling for Transferability Estimation of Vision Foundation Models](https://mlsanity.com/paper/2510.23145/) (2025)
- [MetaRank: Task-Aware Metric Selection for Model Transferability Estimation](https://mlsanity.com/paper/2511.21007/) (2025)
- [Data Distribution Valuation Using Generalized Bayesian Inference](https://mlsanity.com/paper/2604.05993/) (2026)

## Abstract (arXiv)

This paper addresses the problem of ranking pre-trained models for object detection and image classification. Selecting the best pre-trained model by fine-tuning is an expensive and time-consuming task. Previous works have proposed transferability estimation based on features extracted by the pre-trained models. We argue that quantifying whether the target dataset is in-distribution (IND) or out-of-distribution (OOD) for the pre-trained model is an important factor in the transferability estimation. To this end, we propose ETran, an energy-based transferability assessment metric, which includes three scores: 1) energy score, 2) classification score, and 3) regression score. We use energy-based models to determine whether the target dataset is OOD or IND for the pre-trained model. In contrast to the prior works, ETran is applicable to a wide range of tasks including classification, regression, and object detection (classification+regression). This is the first work that proposes transferability estimation for object detection task. Our extensive experiments on four benchmarks and two tasks show that ETran outperforms previous works on object detection and classification benchmarks by an average of 21% and 12%, respectively, and achieves SOTA in transferability assessment.