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Co-translator for Heterogeneous T ransfer L earning

Co-translator for Heterogeneous T ransfer L earning. Problem. Input n images with k labeled instances , k << n. (target domain) A set of English documents ; (source domain) Or a set of audio data . (source domain) Output Image classification function

athena-thompson
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Co-translator for Heterogeneous T ransfer L earning

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  1. Co-translator for Heterogeneous Transfer Learning

  2. Problem • Input • n images with k labeled instances, k << n. (target domain) • A set of English documents; (source domain) • Or a set of audio data. (source domain) • Output • Image classification function • Question : Can we leverage knowledge in heterogeneous sources for improving performance in target?

  3. Previous Work HTLIC : Find a lower image representation by using knowledge in documents and a translator. TTI : learn a similarity function between documents and images by a translator. We define a translator as a set of pairs, each of which contains a image and several words. The translator can be constructed from crowd-sourced web site, such as Flickr, where texts and images co-occur.

  4. Issues in the translator • The crowd-sourced translator is unreliable • (Sparse) Vocabularies used in annotation and documents are too large, while the number of tags for one images is small. It makes the translator sparse; • (Noise); • (Biased) words used in tags are different from that in formal documents.

  5. Solution Let’s express the issues in the other way. That is, the text-image pairs in translator are unreliable. If we can find more reliable pairs, then we will get a high quality translator. And the prediction performance is improved.

  6. Solution (cont.)

  7. Experiment 1

  8. Experiment 2

  9. Experiment 3

  10. Thank You !

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