Neural network architecture

A method, comprising:

• processing an input image using convolution layers to define image features;
• processing the image features to define feature statistics, wherein processing the image features includes applying an activation function in a feature dimension of the image features;
• and
• processing the feature statistics using fully connected layers to produce a binary output regarding a characteristic of the input image,
• wherein processing the image features further includes summing the feature statistics in a first spatial dimension and a second spatial dimension subsequent to applying pooling to the feature statistics, such that the feature statistics include a spatially-summed vector that represents the image features, and
• wherein processing the image features is performed by applying pooling and summing to the feature statistics two or more times in parallel branches, wherein pooling is applied using a different pooling size for each of the parallel branches.

A neural network for processing an input image, comprising:

• convolution layers that accept the input image as an input and produce a feature matrix including values for image features representing presence or absence of the image features in the input image with respect to a first spatial dimension, a second spatial dimension, and a feature dimension;
• a feature analyzer that accepts the feature matrix as an input, applies an activation function in the feature dimension, and produces a spatially-summed feature vector by summing the image features in a first spatial dimension and a second spatial dimension;
• and
• fully connected layers that accept the spatially-summed feature vector as an input and produce a binary output regarding a characteristic of the input image.

A neural network for processing an input image, comprising:

• convolution layers that accept the input image as an input and produce a feature matrix including values for image features representing presence or absence of the image features in the input image with respect to a first spatial dimension, a second spatial dimension, and a feature dimension;
• a feature analyzer that accepts the feature matrix as an input, applies an activation function in the feature dimension, and produces a spatially-summed feature vector;
• and
• fully connected layers that accept the spatially-summed feature vector as an input and produce a binary output regarding a characteristic of the input image,
• wherein the feature analyzer applies pooling subsequent to application of the activation function in the feature dimension,
• wherein the feature analyzer sums the image features in a first spatial dimension and a second spatial dimension subsequent to application of pooling to define the spatially-summed feature vector, and
• wherein the feature analyzer applies pooling and summing to the image features two or more times in parallel branches, wherein pooling is applied using a different pooling size for each of the parallel branches, and values obtained from the parallel branches are combined by concatenation.

A method for training a neural network, comprising:

• processing an input image using convolution layers to define image features;
• processing the image features using feature analysis layers to define feature statistics, wherein processing the image features includes applying a 1×1 convolution to the image features and applying an activation function in a feature dimension of the image features;
• processing the feature statistics using fully connected layers to produce a binary output regarding a characteristic of the input image;
• and
• adjusting at least one of the convolution layers, the feature analysis layers, and the fully connected layers based on a comparison of the binary output to a weak supervision signal that corresponds to the input image.