Image Captioning with Very Scarce Supervised Data:Adversarial Semi-Supervised Learning Approach
Table 2: Comparison with the semi-supervised image captioning method, “Self-Retrieval” Liu et al. (2018). Our method shows improved performance even without Unlabeled-COCO data (denoted as w/o unlabeled) as well as with Unlabeled-COCO (with unlabeled), although our model is not originally proposed for such scenario.
['[EMPTY]', 'BLEU1', 'BLEU2', 'BLEU3', 'BLEU4', 'ROUGE-L', 'SPICE', 'METEOR', 'CIDEr']
For a fair comparison with them, we replace the cross entropy loss from our loss with the policy gradient method Rennie et al. As our baseline model (denoted as Baseline), we train a model only with policy gradient method without the proposed GAN model. When only using the 100% paired MS COCO dataset (denoted as w/o unlabeled), our model already shows improved performance over Self-Retrieval. Moreover, when adding Unlabeled-COCO images (denoted as with unlabeled), our model performs favorably against Self-Retrieval in all the metrics. The results suggest that our method is also advantageous in the semi-supervised setup.