Spatial-frequency channels, shape bias, and adversarial robustness
Authors: Ajay Subramanian, Elena Sizikova, Najib Majaj, Denis Pelli
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we introduce critical band masking as a task for network-human comparison and test 14 humans and 76 neural networks on 16-way Image Net categorization in the presence of narrowband noise. We find that humans recognize objects in natural images using the same one-octave-wide channel that they use for letters and gratings, making it a canonical feature of human object recognition. Unlike humans, the neural network channel is very broad, 2-4 times wider than the human channel. |
| Researcher Affiliation | Academia | Ajay Subramanian New York University as15003@nyu.edu Elena Sizikova New York University es5223@nyu.edu Najib J. Majaj New York University najib.majaj@nyu.edu Denis G. Pelli New York University denis.pelli@nyu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our human and network datasets as well as code required to reproduce our experiments are available publicly at https://github.com/ajaysub110/critical-band-masking. |
| Open Datasets | Yes | In all our experiments, human observers recognized objects in images from Image Net [5], a popular dataset for neural network analysis. |
| Dataset Splits | Yes | We tested 76 pretrained neural networks on the same task that we used to test humans: 16-way categorization of noise-masked images from our critical band masking dataset...Additionally, we also measured the accuracy of all networks on 1000-way classification of all 50,000 available unperturbed Image Net validation set images. |
| Hardware Specification | No | NYU s High Performance Computing (HPC) cluster for enabling our neural network experiments and analysis. |
| Software Dependencies | No | The paper mentions 'Py Torch’s torchvision library', 'Lab JS', 'JATOS', and 'Adversarial Robustness Toolbox' but does not specify their version numbers. |
| Experiment Setup | Yes | Grayscale Image Net images were converted to low-contrast (20%) and perturbed with Gaussian noise that was filtered within various spatial-frequency bands. ... To the preprocessed images, we added Gaussian noise of 5 strengths (standard deviations) (0.0, 0.02, 0.04, 0.08, 0.16), filtered into 7 octave-wide (doubling of frequency) spatial-frequency bands (centered at 1.75, 3.5, 7.0, 14.0, 28.0, 56.0, 128.0 cycles/image)... Images were first resized to 256 256 and center-cropped to 224 224... Images were normalized to Image Net pixel mean and variance... generated adversarial perturbations for 1000 images (each belonging to a different category) from the Image Net validation dataset using projected gradient descent (PGD; L = 0.1, max. iterations = 32). |