Which Model to Transfer? Finding the Needle in the Growing Haystack

13 October 2020

Papers citing "Which Model to Transfer? Finding the Needle in the Growing Haystack"

20 / 20 papers shown

Title
k-NN as a Simple and Effective Estimator of Transferability Moein Sorkhei Christos Matsoukas Johan Fredin Haslum Kevin Smith 52 0 0 24 Mar 2025
Robust and Efficient Transfer Learning via Supernet Transfer in Warm-started Neural Architecture Search Prabhant Singh Joaquin Vanschoren AAML OOD 32 0 0 26 Jul 2024
Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning Pin-Jie Lin Miaoran Zhang Marius Mosbach Dietrich Klakow 30 0 0 23 Jul 2024
A Two-Phase Recall-and-Select Framework for Fast Model Selection Jianwei Cui Wenhang Shi Honglin Tao Wei Lu Xiaoyong Du 38 0 0 28 Mar 2024
Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data A. Abdelmoniem 39 1 0 19 Jun 2023
Model Spider: Learning to Rank Pre-Trained Models Efficiently Yi-Kai Zhang Ting Huang Yao-Xiang Ding De-Chuan Zhan Han-Jia Ye 26 23 0 06 Jun 2023
Green Runner: A tool for efficient model selection from model repositories Jai Kannan Scott Barnett Anj Simmons Taylan Selvi Luís Cruz 20 1 0 26 May 2023
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement Ailin Deng Miao Xiong Bryan Hooi 30 6 0 02 May 2023
Transferability Estimation Based On Principal Gradient Expectation Huiyan Qi Lechao Cheng Jingjing Chen Yue Yu Xue Song Zunlei Feng Yueping Jiang 19 2 0 29 Nov 2022
Selective Cross-Task Distillation Su Lu Han-Jia Ye De-Chuan Zhan 28 0 0 25 Apr 2022
Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts Jiayu Yao Qingyuan Wu Quan Feng Songcan Chen SSL 22 1 0 11 Apr 2022
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning Cédric Renggli Xiaozhe Yao Luka Kolar Luka Rimanic Ana Klimovic Ce Zhang OOD 35 4 0 04 Apr 2022
DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses Alexander Veicht Cédric Renggli Diogo Barradas AAML 25 7 0 08 Mar 2022
What to Pre-Train on? Efficient Intermediate Task Selection Clifton A. Poth Jonas Pfeiffer Andreas Rucklé Iryna Gurevych 14 94 0 16 Apr 2021
Self-Supervised Pretraining Improves Self-Supervised Pretraining Colorado Reed Xiangyu Yue Aniruddha Nrusimha Sayna Ebrahimi Vivek Vijaykumar ... Shanghang Zhang Devin Guillory Sean L. Metzger Kurt Keutzer Trevor Darrell 25 105 0 23 Mar 2021
A linearized framework and a new benchmark for model selection for fine-tuning Aditya Deshpande Alessandro Achille Avinash Ravichandran Hao Li L. Zancato Charless C. Fowlkes Rahul Bhotika Stefano Soatto Pietro Perona ALM 109 46 0 29 Jan 2021
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise Cédric Renggli Luka Rimanic Luka Kolar Wentao Wu Ce Zhang 11 3 0 16 Oct 2020
SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning Colorado Reed Sean L. Metzger A. Srinivas Trevor Darrell Kurt Keutzer SSL 22 49 0 16 Sep 2020
Transferability and Hardness of Supervised Classification Tasks Anh Tran Cuong V Nguyen Tal Hassner 134 164 0 21 Aug 2019
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning Weifeng Ge Yizhou Yu 91 233 0 28 Feb 2017