Image classification neural networks

A neural network system implemented by one or more computers, wherein the neural network system is configured to receive an image and to generate a classification output for the input image, and wherein the neural network system comprises:

• a plurality of subnetworks arranged in a stack on top of each other, wherein each subnetwork is configured to process a subnetwork input to generate a subnetwork output and to provide the subnetwork output as input to another subnetwork above the subnetwork in the stack, and wherein the plurality of subnetworks includes:
• a first subnetwork comprising a plurality of first modules, each first module comprising:
• a first pass-through convolutional layer configured to process the subnetwork input for the first subnetwork to generate a first pass-through output;
• a first average pooling stack of neural network layers, wherein the layers in the first average pooling stack are configured to collectively process the subnetwork input for the first subnetwork to generate a first average pooling output;
• a first stack of convolutional neural network layers, wherein the layers in the first stack are configured to collectively process the subnetwork input for the first subnetwork to generate a first stack output;
• a second stack of convolutional neural network layers, wherein the second stack comprises a 1×1 convolutional layer immediately followed by a 3×3 convolutional layer immediately followed by a 3×3 convolutional layer, and wherein the layers in the second stack are configured to collectively process the subnetwork input for the first subnetwork to generate a second stack output;
• and
• a first concatenation layer configured to concatenate the first pass-through output, the first average pooling output, the first stack output, and the second stack output to generate a first module output for the first module;
• and
• a third subnetwork comprising a plurality of third modules, each third module comprising:
• a third pass-through convolutional layer configured to process the subnetwork input for the third subnetwork to generate a third pass-through output;
• an third average pooling stack of neural network layers, wherein the layers in the third average pooling stack are configured to collectively process the subnetwork input for the third subnetwork to generate an third average pooling output;
• a first group of convolutional neural network layers, wherein the first group including a 1×1 convolutional layer, a 1×3 convolutional layer, and a 3×1 convolutional layer, wherein the layers in the first group are configured to collectively process the subnetwork input for the third subnetwork to generate a first group output;
• a second group of convolutional neural network layers, wherein the layers in the second group are configured to collectively process the subnetwork input for the third subnetwork to generate a second group output;
• and
• a third concatenation layer configured to concatenate the pass-through output, the average pooling output, the first group output, and the second group output to generate a third module output for the third module.

One or more non-transitory storage media encoded with instructions that when executed by one or more computers cause the one or more computers to implement a neural network system that is configured to receive an image and to generate a classification output for the input image, wherein the neural network system comprises:

• a plurality of subnetworks arranged in a stack on top of each other, wherein each subnetwork is configured to process a subnetwork input to generate a subnetwork output and to provide the subnetwork output as input to another subnetwork above the subnetwork in the stack, and wherein the plurality of subnetworks includes:
• a first subnetwork comprising a plurality of first modules, each first module comprising:
• a first pass-through convolutional layer configured to process the subnetwork input for the first subnetwork to generate a first pass-through output;
• a first average pooling stack of neural network layers, wherein the layers in the first average pooling stack are configured to collectively process the subnetwork input for the first subnetwork to generate a first average pooling output;
• a first stack of convolutional neural network layers, wherein the layers in the first stack are configured to collectively process the subnetwork input for the first subnetwork to generate a first stack output;
• a second stack of convolutional neural network layers, wherein the second stack comprises a 1×1 convolutional layer immediately followed by a 3×3 convolutional layer immediately followed by a 3×3 convolutional layer, and wherein the layers in the second stack are configured to collectively process the subnetwork input for the first subnetwork to generate a second stack output;
• and
• a first concatenation layer configured to concatenate the first pass-through output, the first average pooling output, the first stack output, and the second stack output to generate a first module output for the first module;
• and
• a third subnetwork comprising a plurality of third modules, each third module comprising:
• a third pass-through convolutional layer configured to process the subnetwork input for the third subnetwork to generate a third pass-through output;
• an third average pooling stack of neural network layers, wherein the layers in the third average pooling stack are configured to collectively process the subnetwork input for the third subnetwork to generate an third average pooling output;
• a first group of convolutional neural network layers, wherein the first group including a 1×1 convolutional layer, a 1×3 convolutional layer, and a 3×1 convolutional layer, wherein the layers in the first group are configured to collectively process the subnetwork input for the third subnetwork to generate a first group output;
• a second group of convolutional neural network layers, wherein the layers in the second group are configured to collectively process the subnetwork input for the third subnetwork to generate a second group output;
• and
• a third concatenation layer configured to concatenate the pass-through output, the average pooling output, the first group output, and the second group output to generate a third module output for the third module.

A computer-implemented method comprising:

• processing an input image using a neural network system to generate a classification output for the input image, wherein the neural network system comprises a plurality of subnetworks arranged in a stack on top of each other, wherein each subnetwork is configured to process a subnetwork input to generate a subnetwork output and to provide the subnetwork output as input to another subnetwork above the subnetwork in the stack, and wherein the plurality of subnetworks includes:
• a first subnetwork comprising a plurality of first modules, each first module comprising:
• a first pass-through convolutional layer configured to process the subnetwork input for the first subnetwork to generate a first pass-through output;
• a first average pooling stack of neural network layers, wherein the layers in the first average pooling stack are configured to collectively process the subnetwork input for the first subnetwork to generate a first average pooling output;
• a first stack of convolutional neural network layers, wherein the layers in the first stack are configured to collectively process the subnetwork input for the first subnetwork to generate a first stack output;
• a second stack of convolutional neural network layers, wherein the second stack comprises a 1×1 convolutional layer immediately followed by a 3×3 convolutional layer immediately followed by a 3×3 convolutional layer, and wherein the layers in the second stack are configured to collectively process the subnetwork input for the first subnetwork to generate a second stack output;
• and
• a first concatenation layer configured to concatenate the first pass-through output, the first average pooling output, the first stack output, and the second stack output to generate a first module output for the first module;
• and
• a third subnetwork comprising a plurality of third modules, each third module comprising:
• a third pass-through convolutional layer configured to process the subnetwork input for the third subnetwork to generate a third pass-through output;
• an third average pooling stack of neural network layers, wherein the layers in the third average pooling stack are configured to collectively process the subnetwork input for the third subnetwork to generate an third average pooling output;
• a first group of convolutional neural network layers, wherein the first group including a 1×1 convolutional layer, a 1×3 convolutional layer, and a 3×1 convolutional layer, wherein the layers in the first group are configured to collectively process the subnetwork input for the third subnetwork to generate a first group output;
• a second group of convolutional neural network layers, wherein the layers in the second group are configured to collectively process the subnetwork input for the third subnetwork to generate a second group output;
• and
• a third concatenation layer configured to concatenate the pass-through output, the average pooling output, the first group output, and the second group output to generate a third module output for the third module.