Weakness: - (4) or (5) are nonconvex saddle point problems, there is no convergence guarantee for Alg 1. Moreover, as a subroutine for (7), it is not clear how many iterations (the hyperparameter n) should be taken to make sure (7) is convergent. Previously in structured SVM, people noticed that approximate inference could make the learning diverges. - Performance boost due to more parameters? In Tab 1,2,3, if we think carefully, LinearTop and NLTop adds additional parameters, while Unary performs much worse comparing to the numbers reported e.g. in [14], where they used a different and probably better neural network. This raises a question: if we use a better Unary baseline, is there still a performance boost? - In Table 1, the accuracies are extremely poor: testing accuracy = 0.0? Something must be wrong in this experiment. - Scalability: since H(x,c) outputs the whole potential vector with length O(K^m), where m is the cardinality of the largest factor, which could be extremely long to be an input for T. - The performance of NLTop is way behind the Oracle (which uses GT as input for T). Does this indicate (3) is poorly solved or because of the learning itself? [*] N Komodakis Efficient training for pairwise or higher order CRFs via dual decompositio. CVPR 2011. [**] D Sontag et al. Learning efficiently with approximate inference via dual losses. ICML 2010.
- Performance boost due to more parameters? In Tab 1,2,3, if we think carefully, LinearTop and NLTop adds additional parameters, while Unary performs much worse comparing to the numbers reported e.g. in [14], where they used a different and probably better neural network. This raises a question: if we use a better Unary baseline, is there still a performance boost?