Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference-Reference-Cited by-同舟云学术

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

Published:2022-06 Issue: Volume: Page:
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Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Hu Shell Xu¹,Li Da¹,Stuhmer Jan¹,Kim Minyoung¹,Hospedales Timothy M.¹

Affiliation:

1. Samsung AI Center,Cambridge

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879354.pdf?arnumber=9879354

Reference66 articles.

1. A flash-based multi-bit content-addressable memory with euclidean squared distance;kazemi;IEEE/ACM International Symposium on Low Power Electronics and Design,2021

2. Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

3. Deep Residual Learning for Image Recognition

4. A broader study of cross-domain few-shot learning;guo;ECCV,2020

5. Scaling and Benchmarking Self-Supervised Visual Representation Learning

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