SQUEEZENET
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Compelling Advantages
- Smaller CNNs require less communication across servers during distributed training.
- Smaller CNNs require less bandwidth to export a new model from the cloud to an autonomous car.
- Smaller CNNs are more feasible to deploy on FPGAs and other hardware with limited memory.
- SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.
- More efficient distributed training
- Less overhead when exporting new models to clients
Architectural design strategies
- Replace 3x3 filters with 1x1 filters
- Decrease the number of input channels to 3x3 filters
- Downsample late in the network so that convolution layers have large activation maps
Methods
Architecture
Other squeeznet details
Experiments
Others
- early layers in the network have large strides, then most layers will have small activation maps.
- delayed downsampling to four different CNN architectures, and in each case delayed downsampling led to higher classification accuracy
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