- The proposed gradient unrolling method requires N steps to unrolling the gradient, which is slow and perhaps difficult to scale up to learning large and complicated EBLVMs. Although corollary 3 indicates that the estimation accuracy can be asymptoticly arbitrarily small, that requires N to be sufficiently large that may exceed the computing limit. - For comparison, at least one NCE-based method should be included. [1] shows that with a strong noise distribution, this line of work is possible to learn EBM on natural images. - In Table 2, it seems that higher dimension of h leads to worse result. Possible reason needs to be discussed. - Figure 4 shows that the learning can be unstable. [1] Flow Contrastive Estimation of Energy-Based Models
- For comparison, at least one NCE-based method should be included. [1] shows that with a strong noise distribution, this line of work is possible to learn EBM on natural images.