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 | Conference 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 cfchen@cs.duke.edu Oscar Li Duke University oscarli@alumni.duke.edu Chaofan Tao Duke University chaofan.tao@duke.edu Alina Jade Barnett Duke University abarnett@cs.duke.edu Jonathan Su MIT Lincoln Laboratory su@ll.mit.edu Cynthia Rudin Duke University cynthia@cs.duke.edu |
| 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). |