Venue: NeurIPS 2022 Workshop TSRML

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary: The paper introduces a pipeline for training sparse and low-dimensional neural
networks which have increased local and global interpretability over
non-sparse/high-dimensional networks whilst having only a slightly reduce
accuracy. The pipeline comprises the steps of training a neural network using a
novel loss function and employing glm-saga to derive a sparse classifier for a
small number of selected features. Experimental analysis evaluates the method
on four benchmarks.
strengths: Well written and presented. Even though I am not an expert in the area I could
easily follow the main concepts concerning the paper.

Good experimental analysis on common benchmarks, showing  competitive accuracy
and interpretability.
weaknesses: Overall contribution is incremental to previous work.
overall_recommendation: This is a solid, well-written contribution for the derivation of neural network
models with increased interpretability.  While in my opinion is incremental to
previous work, this is an opinion with low confidence as I am not familiar with
related work.
review_title: The paper introduces a novel training scheme for interpretable neural network models.

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: summary: This papers studies interpretability of deep neural networks by learning a sparse and low dimensional final decision layer. The authors propose a pipeline for learning low-dimensional representation (5 dimensions), as well as a new loss function for learning diverse features  which generalizes the previous technique in [Wong et al., 2021] (glm-saga).
strengths: This proposed new pipeline largely improves both the sparsity and the accuracy compared to previous interpretable approach, and the number of features could be as small as 5 for interpreting the model predictions.
weaknesses: There is still a generalization gap (w.r.t. test accuracy) between the sparse model and the dense model, which could be an interesting future direction.
overall_recommendation: I think this paper makes important contributions to interpretable image classification tasks, I recommend accept.
review_title: Review

final_score: rating: 4: Ok but not good enough - rejection
review_content: summary: This paper aims to make deep neural network decisions more interpretable. Typically deep neural networks rely on many features to make predictions (for example the latent representations of Lenet trained on MNIST, vectorized and concatenated would have thousands of dimensions). This paper proposes a new model named Sparse Low-Dimensional Decision “SLDD” model which makes predictions through a low dimensional sparse represenation and corresponding decision layer (as opposed to a dense final decision layer with a high dimensional input). 

The authors first train a deep neural network with a proposed feature diversity loss until convergence. Then they average pool all feature maps to create a feature vector. They then train a series of n sparse linear classifiers with different sparsity levels and using the path algorithm and a variant of variance reduced gradient descent. These classifiers are used both actual classification, as well as for debugging the neural network and interpreting it's predictions. 

A number of experiments is presented on the Resnet50, DenseNet121 and Inception-v3 architectures. The datasets used were the ImageNet-1K, CUB-2011, Stanford Cars, FGVC-Aircraft, and NABirds datasets. The proposed approach is shown to achieve comparable or better accuracy than other approaches and the effect on interpretability is discussed in section 4.3.
strengths: The paper is written in a relatively clear manner, although the discription of the proposed model was (a bit) confusing. The authors provide a range of experiments that I think is sufficient for a workshop submission. the problem that the authors try to tackle is revelant, as currently there is still no accepted solution on how to make neural networks interpretable. Finally the proposed diversity loss seems to improve test accuracy.
weaknesses: I am primarily concerned about the novelty of the work, and indeed the whole line of works starting with glm-saga. While I know that the glm-saga paper was published in ICML 2021, sparsifying neural network layers, feature maps etc has been tried extensively in the previous 5-7 years, and it is difficult for me to see the novelty in this work as well as the work it is based on (apart from a relatively useful application). Furthermore the motivation behind the feature diversity loss is somewhat unclear to me, as well as its impact on interpretability. However I note that I am not an expert on interpretability and have adjusted my confidence score accordingly.
overall_recommendation: I recommend rejecting this paper, as I think that the proposed solution to improving interpretability does not provide sufficient new intuition. I encourage the authors to explore, perhaps with a simplified theoretical model, when and how the proposed approach could be useful.
review_title: Sparsity for interpretability seems insufficient

Decision: Accept