Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Turbo Learning for Captionbot and Drawingbot   We study in this paper the problems of both image captioning and
text-to-image generation, and present a novel turbo learning approach to
jointly training an image-to-text generator (a.k.a. CaptionBot) and a
text-to-image generator (a.k.a. DrawingBot). The key idea behind the joint
training is that image-to-text generation and text-to-image generation as dual
problems can form a closed loop to provide informative feedback to each other.
Based on such feedback, we introduce a new loss metric by comparing the
original input with the output produced by the closed loop. In addition to the
old loss metrics used in CaptionBot and DrawingBot, this extra loss metric
makes the jointly trained CaptionBot and DrawingBot better than the separately
trained CaptionBot and DrawingBot. Furthermore, the turbo-learning approach
enables semi-supervised learning since the closed loop can provide
pseudo-labels for unlabeled samples. Experimental results on the COCO dataset
demonstrate that the proposed turbo learning can significantly improve the
performance of both CaptionBot and DrawingBot by a large margin.

8
The categories of this paper are: hep-ex
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The categories of this paper are: stat
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
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The categories of this paper are: nlin
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an