Contents
Table 6.7: Comparison between different input word features: binary and tf−idf, on Flickr30K data. In all case size of input vocabulary is 50,000 and the inputs are either 2g, 3g, or tk3, and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of mbedding dimension.
['[BOLD] n-gram features comparison Models', '[BOLD] n-gram features comparison Evaluation Metric', '[BOLD] n-gram features comparison Evaluation Metric', '[BOLD] n-gram features comparison Evaluation Metric', '[BOLD] n-gram features comparison Evaluation Metric']
Following this we also experimented on using either binary or tf-idf valued features. Intuition suggests that tf-idf encode more information and so should perform better than binary features. However, empirical evidence suggests otherwise as binary features performed better in most of our models. Hence for future models we stick with binary features.