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So far, research to generate captions from images has been carried out fromthe viewpoint that a caption holds sufficient information for an image. If itis possible to generate an image that is close to the input image from agenerated caption, i.e., if it is possible to generate a natural languagecaption containing sufficient information to reproduce the image, then thecaption is considered to be faithful to the image. To make such regenerationpossible, learning using the cycle-consistency loss is effective. In thisstudy, we propose a method of generating captions by learning end-to-end mutualtransformations between images and texts. To evaluate our method, we performcomparative experiments with and without the cycle consistency. The results areevaluated by an automatic evaluation and crowdsourcing, demonstrating that ourproposed method is effective.

End-to-End Learning Using Cycle Consistency for Image-to-Caption Transformations