Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Authors: Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the efficacy of our architectures against several strong baselines on CIFAR-10, CIFAR-100 and STL-10 with superior or on-par state-of-the-art performance. |
| Researcher Affiliation | Collaboration | 1University of British Columbia, Vancouver, Canada. (bchang@stat.ubc.ca, menglili@cs.ubc.ca, haber@math.ubc.ca) 2Xtract Technologies Inc., Vancouver, Canada. (david@xtract.ai, elliot@xtract.ai) 3Emory University, Atlanta, USA. (lruthotto@emory.edu). |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a link to source code or an explicit statement about releasing it. |
| Open Datasets | Yes | The CIFAR-10 dataset (Krizhevsky and Hinton 2009) consists of 50,000 training images and 10,000 testing images in 10 classes with 32 32 image resolution. |
| Dataset Splits | No | The paper specifies training and test set sizes but does not explicitly mention a separate validation set split or its size. |
| Hardware Specification | Yes | The CIFAR-10/100 and STL-10 experiments are evaluated on a desktop with an Intel Quad-Core i5 CPU and a single Nvidia 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'TensorFlow library' but does not specify a version number. |
| Experiment Setup | Yes | The learning rate is initialized to be 0.1 and decayed by a factor of 10 at 80, 120 and 160 training epochs. The total training step is 80K. The weight decay constant is set to 2 10 4, weight smoothness decay is 2 10 4 and the momentum is set to 0.9. |