reviews: 
Comments: The paper proposes an interesting re-formulation of the knowledge distillation setup from a mixture of experts perspective by putting up the arguement that the distillation from teacher might not be well aligned from the classification objective under some circumstances. Therefore, they treat the teacher prediction label as a latent variable and instead rewrite the classification objective in form of a mixture of experts setup where the prediction conditioned on a particular value of the teacher prediction and the input is treated as one of the experts that classify one of the samples and the prediction that  a given input results in a particular value of teacher label acts as a gating function. Both these are parameterized as neural networks where the gating function is just the teacher's classifier on top of student extracted features and a transformation to the feature space of teacher. Furthermore, they formulate the probability of a sample being recognized as a particular class by a particular expert as a transformation of student feature via a learnable weight matrix and a bias depending to this and expert prototype followed by softmax activation. Furthermore, they adapt it to the KD setup by using the teacher logit and feature. They further derive an evidence lower bound to ease the optimization of their reformulation of the classification objective as MOE one. They further pose this MOE based knowledge distillation as an expectation maximization and also discuss its convergence analysis. 

Furthermore, to validate their formulation, they perform a nummber of experiments on the benchmark datasets including CIFAR-100, ImageNet-1K etc and compare with existing KD baselines.
Strengths: The issue highlighted in the paper is critical in cases where the teacher is not trained at a very large scale to show good generalization. The reformulation of the problem seems interesting and as mentioned in the paper that although there have been some papers that have utilized these setups jointly, the contribution of this paper by treating students classifier as an MoE layer with the teacher's knowledge is novel and intuitive. Overall method of reformulating classification and then deriving ELBO corresponding to eq. 4 and connecting this to expectation maximization also seems sound and interesting. Experimental analysis also helps in verifying the author claims on the widely popular CIFAR-100 and ImageNet-1K benchmarks which are quite popular in the distillation literature. Multiple architectures have been used as teacher student pairs for analysis along with also showing results on a Linear Probe setup and standard deviations for one of the results have also been provided in the appendix which is also useful to actually understand the quality of results.
Weaknesses: I have some major concerns on the experimental part of this which puts the utility/neccessity of this new formulation to large scale foundation models into question. 
1.) In Table 1 and 3 for CIFAR-100 (which is one of the main results) the gains are always less than 1\% except for one case. In the other main result on ImageNet-1K either the gain is less than 1\% or not the best performer only in the top-1Acc. Similar is the case for linear probe experiment (Table 5). Table 6 does have more than 1\% gain in some of the cases but the two datasets are not as large scale and enough to actually conclude that the scheme is effective. In appendix they provide results on CIFAR-100 with std which further increases this concern that can less than 1\% improved accuracy actually advocate for its utility.

2.) Better arhictectures could have been used in the setup as against these older ones. May be like ViTs or pretrained ones like CLIP, LiTs as teachers/students to just understand the effectiveness of this scheme in the lately trending setup. And on similar lines results on more larger scale datasets would be have been pretty useful to understand the implications. 

3.) Since no results are provided on the efficiency, we can't even conclude how much useful w.r.t. compute or memory this method is and can solely judge it on the task numbers which are not very convincing. Therefore, evon though the technical contribution is novel I still can't comprehend as to how it can actually be of much use.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 2