Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
FractalNet: Ultra-Deep Neural Networks without Residuals
Authors: Gustav Larsson, Michael Maire, Gregory Shakhnarovich
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and Image Net classification tasks |
| Researcher Affiliation | Academia | Gustav Larsson University of Chicago EMAIL Michael Maire TTI Chicago EMAIL Gregory Shakhnarovich TTI Chicago EMAIL |
| Pseudocode | No | The paper describes the fractal network expansion rule and block structure using diagrams and mathematical formulas but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about the release of the source code for the methodology described. |
| Open Datasets | Yes | Section 4 provides experimental comparisons to residual networks across the CIFAR-10, CIFAR-100 (Krizhevsky, 2009), SVHN (Netzer et al., 2011), and Image Net (Deng et al., 2009) datasets. |
| Dataset Splits | Yes | Table 2: Image Net (validation set, 10-crop). |
| Hardware Specification | No | We gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research. |
| Software Dependencies | No | We implement Fractal Net using Caffe (Jia et al., 2014). |
| Experiment Setup | Yes | For experiments using dropout, we fix drop rate per block at p0%, 10%, 20%, 30%, 40%q, similar to Clevert et al. (2016). Local drop-path uses 15% drop rate across the entire network. We run for 400 epochs on CIFAR, 20 epochs on SVHN, and 70 epochs on Image Net. Our learning rate starts at 0.02 (for Image Net, 0.001) and we train using stochastic gradient descent with batch size 100 (for Image Net, 32) and momentum 0.9. |