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l2tl

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

Updated implementation of L2TL.

5 лет назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

mnist_pretrain.sh

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

requirements.txt

Updated implementation of L2TL.

5 лет назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

scratch_svhn.sh

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor". https://arxiv.org/abs/2312.07661

9 месяцев назад

README.md

Codebase for "Learning to Transfer Learn"

Authors: Linchao Zhu, Sercan O. Arik, Yi Yang, and Tomas Pfister

Paper: https://arxiv.org/abs/1908.11406

Learning to transfer learn (L2TL), to improve transfer learning on a target dataset by judicious extraction of information from a source dataset.

This repository contains an example implementation of L2TL framework on the task of transferring knowledge from MNIST to SVHN.

To run the experiments that compare training from random initialization, fine-tuning and L2TL, run bash run.sh.

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