Venue: ICML 2021 Workshop INNF

Reviews:

final_score: rating: Accept
review_content: summary: This paper proposes distilling a larger (teacher) normalizing flow into a smaller student model, which does not necessarily have a flow's bijectivity constraints.  The loss function for the distillation is a composite of reconstruction and perceptual distance terms.  Experiments are reported on super resolution and speech synthesis tasks, which show good performance from the student models.    
Justification for rating: This paper proposes the very reasonable idea of distilling normalizing flows into smaller, more general architectures.  The idea has clear motivations from practice, and the experiments suggest that the idea is realizable.  Moreover, the topic is appropriate for the workshop.
review_title: null

final_score: rating: Accept
review_content: summary: This paper introduces a method to train a more efficient model from a pre-trained normalizing flow. The model is called a student model and the pre-trained normalizing flow is referred to as the teacher. The student model is not restricted on invertibility nor on tractability of the Jacobian. In this manner the model has less computational cost and is faster during inference time, while achieving comparable performance as the pre-trained model.
Justification for rating: This method is useful for maintaining performance at a lower cost than a pre-trained normalizing flow. The overall paper is written well in principle but misses more explanation of the method in the main paper.
review_title: null

Decision: Accept (spotlight talk)