Review for NeurIPS paper: What shapes feature representations? Exploring datasets, architectures, and training

Weaknesses: One of the main concerns is the size of the dataset used from training (4900 images). To train a deep architecture, this size is very small. It is well-known that deep models using smaller datasets often result in a lower test accuracy, perhaps because the training set is not sufficiently representative of the problem and the model might overfit. To address this, researchers have of the used transfer learning (pre-trained base CNNs that are trained over the large diverse datasets) and fine-tuned on the smaller dataset. Given the size of the AlexNet (61M parameters), I have a feeling that the model is overfitted for this particular experimental design and evaluation.