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Exploring and analyzing feature representations in TL with Interactive Visualization.

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Transfer Learning Common Knowledge Extractor

TransferVis: A visualization tool for exploring and analyzing feature representations in transfer learning, enhancing interpretability and decision-making.

Credits to original creator

CNN Explainer was created by Jay Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, and Polo Chau, which was the result of a research collaboration between Georgia Tech and Oregon State.

Citation

@article{wangCNNExplainerLearning2020,
  title = {{{CNN Explainer}}: {{Learning Convolutional Neural Networks}} with {{Interactive Visualization}}},
  shorttitle = {{{CNN Explainer}}},
  author = {Wang, Zijie J. and Turko, Robert and Shaikh, Omar and Park, Haekyu and Das, Nilaksh and Hohman, Fred and Kahng, Minsuk and Chau, Duen Horng},
  journal={IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  year={2020},
  publisher={IEEE}
}

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Exploring and analyzing feature representations in TL with Interactive Visualization.

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  • JavaScript 58.3%
  • Svelte 36.0%
  • Python 4.8%
  • Other 0.9%