White Noise Analysis of Neural Networks
Authors: Ali Borji, Sikun Lin
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments over four classic datasets, MNIST, Fashion-MNIST, CIFAR-10, and Image Net, show that the computed bias maps resemble the target classes and when used for classification lead to an over two-fold performance than the chance level. |
| Researcher Affiliation | Collaboration | Ali Borji & Sikun Lin University of California, Santa Barbara, CA aliborji@gmail.com, sikun@ucsb.edu Work done during internship at Markable AI. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/aliborji/White Noise Analysis.git. |
| Open Datasets | Yes | Over four datasets, MNIST (Le Cun et al., 1998), Fashion-MNIST (Xiao et al., 2017), CIFAR-10 (Krizhevsky et al., 2009), and Image Net Deng et al. (2009), we employ classification images to discover implicit biases of a network... |
| Dataset Splits | Yes | We conducted an experiment on Image Net validation set including 50K images covering 1000 categories and 1 million samples using Gabor PCA sampling... |
| Hardware Specification | No | The paper mentions running experiments on 'a single GPU' but does not specify the model or manufacturer (e.g., NVIDIA A100, RTX 2080 Ti), making the hardware specification too vague. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We trained a CNN with 2 conv layers, 2 pooling layers, and one fully connected layer (see supplement Fig. 10) on the MNIST dataset. ... We changed the activation functions in the convolution layers of the CIFAR-10 CNN model to tanh, as using Re LU activation resulted in some dead filters. ... here we use λl = 0.01, 0.1, 1 for fc, conv1, and conv2, respectively. |