The methodology appears incremental, building marginally upon JEM's foundation of interpreting classifiers as time-dependent EBMs. The newly introduced self-calibration loss primarily enhances this by applying a standard DSM technique to train the internal score function, thus lacking substantial novelty.
The authors have judiciously selected a range of baseline candidates for semi-supervised learning and reported performance results. However, the focus on datasets like CIFAR-10 and CIFAR-100 limits the assessment of the methodology's generalizability to more diverse or complex data scenarios.
* Minor weaknesses
The allocation of Figure 1 is too naive. Overall, you could have edited the space of main paper more wisely.
* Minor weaknesses The allocation of Figure 1 is too naive. Overall, you could have edited the space of main paper more wisely.