# US 2018/0322368 A1 · System an method for batch-normalized recurrent highway networks

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- Number: US 2018/0322368 A1
- Document ID: US20180322368A1
- Status: Published application
- Kind: A1
- Application number: 15/968,012
- Filed: 2018-05-01
- Published: 2018-11-08
- Earliest claimed: 2017-05-02
- Assignees: Kodak Alaris Inc.
- Applicants: Kodak Alaris Inc.
- Inventors: Chi ZHANG; Raymond PTUCHA; Alexander LOUI; Carl SALVAGGIO
- Web page: https://patentgrep.com/patent/US20180322368A1

## CPC

- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- G06F 18/24143: {Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]}
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/776: Validation; Performance evaluation
- G06V 10/82: using neural networks
- G06N 3/045: Combinations of networks
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Embodiments of the present disclosure relate to a recurrent framework based on Recurrent Highway Networks (RHNs) for sequence modeling using batch normalization. In certain embodiments, constraints within the RHNs are relaxed to reduce or avoid gradient vanishing or exploding by normalizing the current transition units in highway layers.

## Claims

### Claim 1

A computer-implemented method, comprising:
loading a pre-trained model into a computing network;
receiving a digital image input, the digital image input containing one or more objects;
batch normalizing the digital image input;
applying one or more non-linear transforms to the digital input image to generate one or more transform outputs from the computing network; and
combining the one or more transform outputs to generate a final output from the computing network.

### Claim 2 (depends on 1)

The method of claim 1 wherein the pre-trained model contains parameters for the computing network nodes.

### Claim 3 (depends on 2)

The method of claim 2 wherein the computing network is a multi-layer neural network.

### Claim 4 (depends on 3)

The method of claim 3 wherein the multi-layer neural network is a Recurrent Highway Network.

### Claim 5 (depends on 4)

The method of claim 4 wherein the multi-layer neural network is a batch-normalized Recurrent Highway Network.

### Claim 6 (depends on 5)

The method of claim 5 wherein the constraints within the Recurrent Highway Network are relaxed to reduce or avoid gradient vanishing or exploding by normalizing the current transition units in highway layers.

### Claim 7 (depends on 1)

The method of claim 1 wherein the final output contains a caption describing the content of the digital image input.

### Claim 8

A computer-implemented method, comprising:
receiving a digital image input, the digital image input containing one or more objects;
batch normalizing the digital image input;
applying one or more non-linear transforms to the digital input image to generate one or more transform outputs; and
combining the one or more transform outputs to generate a final output.

### Claim 9 (depends on 8)

The computer-implemented method of claim 8, wherein batch normalizing occurs at one or more highway layers of a recurrent highway network.

### Claim 10 (depends on 9)

The computer-implemented method of claim 9, wherein the one or more highway layers comprise a transform gate and a carry gate.

### Claim 11 (depends on 10)

The computer-implemented method of claim 10, wherein the transform gate is coupled to the carry gate.

### Claim 12 (depends on 9)

The computer-implemented method of claim 9, wherein the digital image input is an output of a prior processing loop in the recurrent highway network.

### Claim 13 (depends on 8)

The computer-implemented method of claim 8, wherein batch normalizing the digital image input comprises extracting a mean and a variance across each channel and spatial location.

### Claim 14 (depends on 13)

The computer-implemented method of claim 13, wherein batch normalizing the digital image input comprises subtracting the mean of the digital image input and dividing by the variance of the digital image input.