An Inception Module is an image model block that aims to approximate an optimal local sparse structure in a CNN. Put simply, it allows for us to use multiple types of filter size, instead of being restricted to a single filter size, in a single image block, which we then concatenate and pass onto the next layer.
Source: Going Deeper with ConvolutionsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Classification | 31 | 12.16% |
General Classification | 30 | 11.76% |
Classification | 21 | 8.24% |
Object Detection | 18 | 7.06% |
Quantization | 13 | 5.10% |
Object Recognition | 8 | 3.14% |
Autonomous Driving | 5 | 1.96% |
Domain Adaptation | 5 | 1.96% |
Image Generation | 4 | 1.57% |
Component | Type |
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Convolutions | |
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Convolutions | |
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Pooling Operations |