Designing Counterfactual Generators using Deep Model Inversion
Authors: Jayaraman Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jia Liang, Akshay Chaudhari, Andreas Spanias
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies using natural image and medical image classifiers to demonstrate the effectiveness of DISC over a variety of baselines and ablations. |
| Researcher Affiliation | Collaboration | Jayaraman J. Thiagarajan Lawrence Livermore National Laboratory jjayaram@llnl.gov Vivek Narayanaswamy Arizona State University vnaray29@asu.edu Deepta Rajan IBM Research AI r.deepta@gmail.com Jason Liang Stanford University jialiang@stanford.edu Akshay Chaudhari Stanford University akshaysc@stanford.edu Andreas Spanias Arizona State University spanias@asu.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Datasets. (i) Celeb A Faces [31]: This dataset contains 202,599 images along with a wide-range of attributes... (ii) ISIC 2018 Skin Lesion Dataset [32]: This lesion diagnosis challenge dataset contains a total of 10, 015 dermoscopic lesion images... |
| Dataset Splits | No | Note, in all cases, we used a stratified 90 10 data split to train the classifiers. |
| Hardware Specification | No | The paper mentions "Lawrence Livermore National Laboratory" but does not provide specific hardware details such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper mentions ResNet-18 and Adam optimizer but does not provide specific software version numbers for libraries or frameworks used. |
| Experiment Setup | Yes | For all experiments, we resized the images to size 96 96 and used the standard Res Net-18 architecture [34] to train the classifier model with the Adam optimizer [35], batch size 128, learning rate 1e 4 and momentum 0.9. For the DEP implementation (Section 3.3), we performed average pooling on feature maps from each of the residual blocks in Res Net-18, and applied a linear layer of 128 units with Re LU activation. The hyper-parameters in (7) were set at β1 = 1.0 and β2 = 0.5. For the case of DUQ, we set both the length scale parameter and the gradient penalty to 0.5. |