ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network
The authors propose a set of design principles that improves model performance significantly based on the analysis of representation bottlenecks.
Authors think that commonly used architectures have a representation bottleneck and try to fix it by expanding channel size, using more expand layers, and better activation functions. This also improves the performance of models on ImageNet and good results on transfer learning on classification and object detection.
Authors hope that their design ideas could be used by NAS to create even better models.
Paper: https://arxiv.org/abs/2007.00992
Code: https://github.com/clovaai/rexnet
#deeplearning #pretraining #transferlearning #computervision #pytorch
The authors propose a set of design principles that improves model performance significantly based on the analysis of representation bottlenecks.
Authors think that commonly used architectures have a representation bottleneck and try to fix it by expanding channel size, using more expand layers, and better activation functions. This also improves the performance of models on ImageNet and good results on transfer learning on classification and object detection.
Authors hope that their design ideas could be used by NAS to create even better models.
Paper: https://arxiv.org/abs/2007.00992
Code: https://github.com/clovaai/rexnet
#deeplearning #pretraining #transferlearning #computervision #pytorch