Image Captioning with Sparse Recurrent Neural Network
Table 5: Comparison with dense LSTM and GRU baselines after CNN fine-tuning.
['Approaches', 'NNZ parameters RNN size', 'NNZ parameters Overall CR', 'MS-COCO test set scores B-1', 'MS-COCO test set scores B-2', 'MS-COCO test set scores B-3', 'MS-COCO test set scores B-4', 'MS-COCO test set scores M', 'MS-COCO test set scores R', 'MS-COCO test set scores C', 'MS-COCO test set scores S']
In this section, we investigate the potential impact of fine-tuning the entire captioning model in an end-to-end manner. This phenomenon is especially apparent on very sparse models with sparsity at 97.5%. On both LSTM and GRU models, the drops in performance suffered due to pruning have mostly reduced except for LSTM at 80% sparsity.