Gradient Origin Networks
Authors: Sam Bond-Taylor, Chris G. Willcocks
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show that the proposed method converges faster, with significantly lower reconstruction error than autoencoders, while requiring half the parameters. |
| Researcher Affiliation | Academia | Sam Bond-Taylor & Chris G. Willcocks Department of Computer Science Durham University {samuel.e.bond-taylor,christopher.g.willcocks}@durham.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code for the convolutional GON, variational GON, and implicit GON is available under the MIT license on Git Hub at: https://github.com/cwkx/GON. |
| Open Datasets | Yes | We evaluate Gradient Origin Networks on a variety of image datasets: MNIST (Le Cun et al., 1998), Fashion-MNIST (Xiao et al., 2017), Small NORB (Le Cun et al., 2004), COIL-20 (Nane et al., 1996), CIFAR-10 (Krizhevsky et al., 2009), Celeb A (Liu et al., 2015), and LSUN Bedroom (Yu et al., 2015). |
| Dataset Splits | Yes | Table 1: Validation reconstruction loss (summed squared error) over 500 epochs. Table 2: Validation ELBO in bits/dim over 1000 epochs (Celeb A is trained over 150 epochs). |
| Hardware Specification | Yes | This implementation uses Py Torch and all reported experiments use a Nvidia RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number for it or any other software dependency. |
| Experiment Setup | Yes | Simple models are used: for small images, implicit GONs consist of approximately 4 hidden layers of 256 units and convolutional GONs consist of 4 convolution layers with Batch Normalization (Ioffe & Szegedy, 2015) and the ELU non-linearity (Clevert et al., 2016), for larger images the same general architecture is used, scaled up with additional layers; all training is performed with the Adam optimiser (Kingma & Ba, 2015). |