In this paper, we propose the DEeP At- tribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs. In DEPARA, nodes.

Mar 17, 2020 · In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs. In DEPARA, ...

We test Task Transferability on Taskonomy Models. For pre-trained models, testing images, please follow the instruction of TransferabilityfromAttributionMaps.

In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs. In DEPARA, nodes correspond ...

We apply DEPARA to two important yet under- studied problems in transfer learning: pre-trained model selection and layer selection. Extensive experiments are.

DEPARA: Deep Attribution Graph for Deep Knowledge Transferability. – ... Firstly, we give detailed descriptions of the probe data used in this paper. Then ...

This research question drives the design of transferability estimation methods, including computationintensive [6][7] [8] [9] and computation-efficient ones ...

Depara: Deep attribution graph for deep knowledge transferability. J Song, Y Chen, J Ye, X Wang, C Shen, F Mao, M Song. Proceedings of the IEEE/CVF Conference ...

As the DEPARAs of different PR-DNNs are defined on the same set of inputs, they are actually in the same embedding space and thus the knowledge transferability ...

DEPARA: Deep Attribution Graph for Deep Knowledge Transferability. Jie Song*, Yixin Chen*, Jingwen Ye, Xinchao Wang, Chengchao Shen, Feng Mao, Mingli Song ...