Do Invariances in Deep Neural Networks Align with Human Perception?
Authors: Vedant Nanda, Ayan Majumdar, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Bradley C. Love, Adrian Weller
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then conduct an in-depth investigation of how different components (e.g. architectures, training losses, data augmentations) of the deep learning pipeline contribute to learning models that have good alignment with humans. Code: github.com/nvedant07/Human-NN-Alignment. We strongly recommend reading the arxiv version of this paper: https://arxiv.org/abs/2111.14726. |
| Researcher Affiliation | Academia | Vedant Nanda1,2, Ayan Majumdar2, Camila Kolling2, John P. Dickerson1, Krishna P. Gummadi2, Bradley C. Love3,4, Adrian Weller3,5 1University of Maryland, College Park, USA 2Max Planck Institute for Software Systems (MPI-SWS), Germany 3The Alan Turing Institute, London, England 4University College London, London, England 5University of Cambridge, Cambridge, England |
| Pseudocode | No | No explicit pseudocode or algorithm blocks labeled as 'Pseudocode' or 'Algorithm' were found. |
| Open Source Code | Yes | Code: github.com/nvedant07/Human-NN-Alignment. |
| Open Datasets | Yes | Feather et al. studied representational invariance for different layers of DNNs trained over Image Net data (using the standard cross-entropy loss). |
| Dataset Splits | No | No specific details on train/validation/test splits, percentages, or cross-validation setup are explicitly provided in the main text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or cloud instance specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions software like 'Py Torch Lightning', 'Pytorch', 'Num Py', 'Matplotlib', and 'robustness (Python Library)' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | This is achieved by performing gradient descent on x0 (in our experiments we use SGD with a learning rate of 0.1) to minimize a loss of the following general form: |