Image Captioning with Sparse Recurrent Neural Network
Table 7: Comparison on caption uniqueness and length with dense baselines on MS-COCO test set.
['Approaches', 'NNZ parameters Sparsity', 'NNZ parameters Overall CR', 'Before fine-tune Unique (%)', 'Before fine-tune Average length', 'After fine-tune Unique (%)', 'After fine-tune Average length']
On the contrary, more unseen captions are being generated at higher levels of sparsity and compression. On the other hand, we can see that the average lengths of generated captions peaked at 80% sparsity in most cases and then decrease slightly as sparsity increase. That being said, the reductions in caption length are minimal (+0.5% to −2.3%) considering the substantial decoder compression rates of up to 40×.