MobileNetv2
New light-weight architecture from Google with 72%+ top1
(0)
Performance https://goo.gl/2czk9t
Link http://arxiv.org/abs/1801.04381
Pre-trained implementation
- https://github.com/tonylins/pytorch-mobilenet-v2
- but this one took much more memory that I expected
- did not debug it
(1)
Gist - new light-weight architecture from Google with 72%+ top1 on Imagenet
Ofc Google promotes only its own papers there
No mention of SqueezeNet
This is somewhat disturbing
(2)
Novel ideas
- the shortcut connections are between the thin bottleneck layers
- the intermediate expansion layer uses lightweight depthwise convolutions
- it is important to remove non-linearities in the narrow layers in order to maintain representational power
(3)
Very novel idea - it is argued that non-linearities collapse some information.
When the dimensionality of useful information is low, you can do w/o them w/o loss of accuracy
(4) Building blocks
- Recent small networks' key features (except for SqueezeNet ones) - https://goo.gl/mQtrFM
- MobileNet building block explanation
- https://goo.gl/eVnWQL https://goo.gl/Gj8eQ5
- Overall architecture - https://goo.gl/RRhxdp
#deep_learning
New light-weight architecture from Google with 72%+ top1
(0)
Performance https://goo.gl/2czk9t
Link http://arxiv.org/abs/1801.04381
Pre-trained implementation
- https://github.com/tonylins/pytorch-mobilenet-v2
- but this one took much more memory that I expected
- did not debug it
(1)
Gist - new light-weight architecture from Google with 72%+ top1 on Imagenet
Ofc Google promotes only its own papers there
No mention of SqueezeNet
This is somewhat disturbing
(2)
Novel ideas
- the shortcut connections are between the thin bottleneck layers
- the intermediate expansion layer uses lightweight depthwise convolutions
- it is important to remove non-linearities in the narrow layers in order to maintain representational power
(3)
Very novel idea - it is argued that non-linearities collapse some information.
When the dimensionality of useful information is low, you can do w/o them w/o loss of accuracy
(4) Building blocks
- Recent small networks' key features (except for SqueezeNet ones) - https://goo.gl/mQtrFM
- MobileNet building block explanation
- https://goo.gl/eVnWQL https://goo.gl/Gj8eQ5
- Overall architecture - https://goo.gl/RRhxdp
#deep_learning