Semantically Invariant Text-to-Image Generation
Table 1: Evaluation of the generated image quality using the inception, detection and human scores on the test set.
['Method', 'Inception', 'Detection', 'Human']
The inception scores indicate the improvement in generated images when BLEU-1 (B-1) and the multiple caption conditioning (Nc=5) are used. The detection scores for multiple captions are significantly better than other variants. However, BLEU-1 is slightly lower than the baseline result. Despite having worse inception scores, baseline methods got higher human evaluation scores. We believe the reason for this trend is lack of detail in objects generated by multiple captions. The baseline model generates images with single objects, making them visually appealing. Was the n-gram scaling useful ? It is very difficult to judge the two techniques visually. We use only a single caption to condition the image generator to have a fair comparison in this case. The BLEU-1 score was used as the word level error multiplier and it scales the gradients accordingly.