What You See is What You Classify: Black Box Attributions
Authors: Steven Stalder, Nathanael Perraudin, Radhakrishna Achanta, Fernando Perez-Cruz, Michele Volpi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our attributions are superior to established methods both visually and quantitatively with respect to the PASCAL VOC-2007 and Microsoft COCO-2014 datasets. and We present three results to establish the advantages of our Explainer over existing approaches. and In Tab. 2 we illustrate the accuracy of the masking strategies, on VOC-2007 and COCO-2014, with VGG-16 and Res Net-50 as Explanandum, respectively. |
| Researcher Affiliation | Academia | Steven Stalder Swiss Data Science Center ETH Zurich, Switzerland Nathanaël Perraudin Swiss Data Science Center ETH Zurich, Switzerland Radhakrishna Achanta Swiss Data Science Center EPFL, Switzerland Fernando Perez-Cruz Swiss Data Science Center ETH Zurich, Switzerland Michele Volpi Swiss Data Science Center ETH Zurich, Switzerland |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Code is available at: https://github.com/stevenstalder/NN-Explainer |
| Open Datasets | Yes | We evaluate our methodology on PASCAL Visual Object Classes (VOC-2007) [9] and Microsoft Common Objects in Context (COCO-2014) [18]. |
| Dataset Splits | Yes | To this end, we use the full training set of VOC-2007 and 90% of the COCO-2014 training set for fine-tuning. To assess generalization, we use the test set of VOC-2007 and the validation set of COCO-2014, respectively. For choosing hyperparameters, we use the VOC-2007 validation set and the remaining 10% of the COCO-2014 training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | In this work, we implemented models and experiments using Py Torch [19] and Py Torch Lightning [10]. Pytorch lightning. Git Hub. Note: https://github. com/Py Torch Lightning/pytorch-lightning, 3:6, 2019. |
| Experiment Setup | Yes | For all our experiments, we resized input images to 224x224 pixels and normalized them on the mean and standard deviation of Image Net. and We formulate the loss as a combination of four terms: LE(x, Y, S, m, n) = Lc(x, Y, m) + λe Le(x, m) + λa La(m, n, S) + λtv Ltv(m, n), where λe, λa and λtv are hyperparameters balancing the loss terms. |