This Looks Like That: Deep Learning for Interpretable Image Recognition
Authors: Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan K. Su
NeurIPS 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that Proto PNet can achieve comparable accuracy with its analogous non-interpretable counterpart, and when several Proto PNets are combined into a larger network, it can achieve an accuracy that is on par with some of the best-performing deep models. We demonstrate our method on the CUB-200-2011 dataset and the Stanford Cars dataset. |
| Researcher Affiliation | Collaboration | Chaofan Chen Duke University EMAIL Oscar Li Duke University EMAIL Chaofan Tao Duke University EMAIL Alina Jade Barnett Duke University EMAIL Jonathan Su MIT Lincoln Laboratory EMAIL Cynthia Rudin Duke University EMAIL |
| Pseudocode | Yes | The entire training algorithm is summarized in an algorithm chart, which can be found in Section S9.3 of the supplement. |
| Open Source Code | Yes | Supplementary Material and Code: The supplementary material and code are available at https: //github.com/cfchen-duke/Proto PNet. |
| Open Datasets | Yes | We trained and evaluated our network on the CUB-200-2011 dataset [45] of 200 bird species. We also trained our Proto PNet on the Stanford Cars dataset [20] of 196 car models. |
| Dataset Splits | No | The paper mentions "using cross validation" for choosing a hyperparameter (D) but does not provide specific details about train/validation/test splits, sample counts, or the methodology of the cross-validation itself. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud computing instances used for experiments. |
| Software Dependencies | No | The paper mentions using convolutional layers from models like VGG-16, VGG-19, ResNet-34, etc., but it does not provide specific version numbers for software libraries, frameworks, or operating systems used in the experiments. |
| Experiment Setup | Yes | For the bird dataset with input images resized to 224x224x3, the spatial dimension of the convolutional output is H = W = 7, and the number of output channels D in the additional convolutional layers is chosen from three possible values: 128, 256, 512, using cross validation. In our Proto PNet, we allocate a pre-determined number of prototypes mk for each class k (10 per class in our experiments). |