Going Deeper with Convolutions
We propose a deep convolutional neural network ar- chitecture codenamed Inception that achieves the new state of the art for classification and detection in the Im- ageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the compu- tational budget constant. To optimize quality, the architec- tural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular in- carnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.
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