Multimodal Storytelling via Generative Adversarial Imitation Learning
Table 1: Similarity performance (T./I. denotes Text/Image respectively, T.I. means the combination of T. and I.)
['Ran.', 'SS', 'PG', 'LSTM']
We evaluate our result with several established alternatives: random, scheduled sampling[bengio2015scheduled] and policy gradient with similarity. Unfortunately, the baselines do not share the same metric or objective function. Instead, they were compared regarding accumulative normalized similarity on the training set: sumsim=∑A∈Data∑B∈modelAB∥A∥F⋅∥B∥F where, sample A denotes sequence matrix from the user-provided data, and B is the output sequence matrix from models.