Model Search Paper Repository

This is a repository contains papers about model search for future transfer learning/fine tune, the problem aims to filter and search pre-trained models before stepping into the real fine-tuning process on the downstream/target task/dataset.

Latest Papers

• Renggli, Cedric, et al. "Which model to transfer? finding the needle in the growing haystack." CVPR2022. [Supplementary Materials]
• Renggli, Cedric, et al. "SHiFT: An Efficient, Flexible Search Engine for Transfer Learning." arXiv preprint arXiv:2204.01457 (2022).

Benchmarks and preliminary research

This section contains some benchmarks/datasets settings which are commonly used in model search/transfer learning papers.

• Zamir, Amir R., et al. "Taskonomy: Disentangling task transfer learning." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [Supplementary Materials]
• Kornblith, Simon, Jonathon Shlens, and Quoc V. Le. "Do better imagenet models transfer better?." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
• Zhai, Xiaohua, et al. "The visual task adaptation benchmark." (2019).

Existing works

This section contains papers in the past years about model search or transferability measurement methods.

Task Agnostic Search

These strategies rank models without looking at the data of the downstream task.

• Qiang, Zhangcheng, et al. "MobileDLSearch: Ontology-based Mobile Platform for Effective Sharing and Reuse of Deep Learning Models." 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). IEEE, 2021.
• Sparks, Evan R., et al. "Automating model search for large scale machine learning." Proceedings of the Sixth ACM Symposium on Cloud Computing. 2015.
• Kornblith, Simon, Jonathon Shlens, and Quoc V. Le. "Do better imagenet models transfer better?." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.

Task Aware Search

Task-aware methods use the downstream data to select models, thus requiring additional computation.

• Ueno, Yosuke, and Masaaki Kondo. "A Base Model Selection Methodology for Efficient Fine-Tuning." (2019).
• Puigcerver, Joan, et al. "Scalable Transfer Learning with Expert Models." International Conference on Learning Representations. 2020. [Supplementary Materials]
• Bao, Yajie, et al. "An information-theoretic approach to transferability in task transfer learning." (H-Score) 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. [Supplementary Materials]
• Tran, Anh T., Cuong V. Nguyen, and Tal Hassner. "Transferability and hardness of supervised classification tasks." (NCE) Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. [Supplementary Materials]
• Nguyen, Cuong, et al. "Leep: A new measure to evaluate transferability of learned representations." International Conference on Machine Learning. PMLR, 2020. [Supplementary Materials]
• Deshpande, Aditya, et al. "A linearized framework and a new benchmark for model selection for fine-tuning." arXiv preprint arXiv:2102.00084 (2021).
• Bolya, Daniel, Rohit Mittapalli, and Judy Hoffman. "Scalable Diverse Model Selection for Accessible Transfer Learning." Advances in Neural Information Processing Systems 34 (2021). [Supplementary Materials]
• You, Kaichao, et al. "Logme: Practical assessment of pre-trained models for transfer learning." International Conference on Machine Learning. PMLR, 2021.

Meta-Learned Task Aware Search

Meta-Learned Task-aware methods’ goal is to favor models that perform well on benchmark datasets similar to the downstream one.

• Cui, Yin, et al. "Large scale fine-grained categorization and domain-specific transfer learning." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
• Achille, Alessandro, et al. "Task2vec: Task embedding for meta-learning." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. [Supplementary Materials]
• Dwivedi, Kshitij, and Gemma Roig. "Representation similarity analysis for efficient task taxonomy & transfer learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
• Dwivedi, Kshitij, et al. "Duality diagram similarity: a generic framework for initialization selection in task transfer learning." European Conference on Computer Vision. Springer, Cham, 2020. [Supplementary Materials]
• Song, Jie, et al. "Deep model transferability from attribution maps." Advances in Neural Information Processing Systems 32 (2019). [Supplementary Materials]
• Song, Jie, et al. "Depara: Deep attribution graph for deep knowledge transferability." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. [Supplementary Materials]