CrossedWires
Introduced by Zvyagin et al. in CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models
CrossedWires is a living dataset of models and hyperparameters that exposes semantic differences between two popular deep learning frameworks: PyTorch and Tensorflow.
The CrossedWires dataset currently consists of models trained on CIFAR10 images using three different computer vision architectures: VGG16, ResNet50 and DenseNet121 across a large hyperparameter space. Using hyperparameter optimization, each of the three models was trained on 400 sets of hyperparameters suggested by the HyperSpace search algorithm.
The CrossedWires dataset includes PyTorch and Tensforflow models with test accuracies as different as 0.681 on syntactically equivalent models and identical hyperparameter choices. The 340 GB dataset and benchmarks presented here include the performance statistics, training curves, and model weights for all 1200 hyperparameter choices, resulting in 2400 total models. The CrossedWires dataset provides an opportunity to study semantic differences between syntactically equivalent models across popular deep learning frameworks.
Papers
| Paper | Code | Results | Date | Stars |
|---|
