Join GitHub today
GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.Sign up
In CrazyAra v0.2.0 a newly designed architecture was used which is called RISE for short.
It incorporates new ideas and techniques described in recent papers for Deep Learning in Computer Vision.
|ResneXt||He et al. - 2015 - Deep Residual Learning for Image Recognition.pdf - https://arxiv.org/pdf/1512.03385.pdf|
|Xie et al. - 2016 - Aggregated Residual Transformations for Deep Neurarl Networks - http://arxiv.org/abs/1611.05431|
|Inception||Szegedy et al. - 2015 - Rethinking the Inception Architecture for ComputerVision - https://arxiv.org/pdf/1512.00567.pdf)|
|Szegedy et al. - 2016 - Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - https://arxiv.org/pdf/1602.07261.pdf)|
|Squeeze||Hu et al. - 2017 - Squeeze-and-Excitation Networks - https://arxiv.org/pdf/1709.01507.pdf)|
|Excitation||Hu et al. - 2017 - Squeeze-and-Excitation Networks - https://arxiv.org/pdf/1709.01507.pdf)|
The proposed model architecture has fewer parameters, faster inference and training time while maintaining an equal amount of depth compared to the architecture proposed by DeepMind (19 residual layers with 256 filters). On our 10,000 games benchmark dataset it achieved a lower validation error using the same learnig rate and optimizer settings.
|RISE-Architecture (CrazyAra v0.2)||Vanilla-Resnet Architecture(CrazyAra v0.1)|