REAL-TO-SYNTHETIC IMAGE DOMAIN TRANSFER
Systems, methods, and machine-readable media for deterministically generating labeled data for training or validating machine learning models for image analysis, and for using such machine learning models to determine the contents of real-domain images by using a domain transfer to synthetic-appearing images are described.
1 . A method for training a machine-learning model to convert real-domain images to synthetic-appearing images, wherein the machine-learning model is associated with a mounted camera device at a location, the location associated with a scene type, the method comprising:
receiving a first set of real-domain training images associated with the scene type;
generating a second set of synthetic-domain training images associated with the scene type;
training the machine-learning model, using the first and second sets of training images, to generate respective synthetic-appearing images based on respective sample real-domain images, wherein the respective synthetic-appearing output images have visual characteristics that are more similar to the visual characteristics of the synthetic-domain training images than to the visual characteristics of the real-domain training characteristics; and
providing the machine-learning model to the mounted camera device.
2 . The method of claim 1 , wherein the first and second sets of training images are unpaired.
3 . The method of claim 1 , wherein the machine-learning model is a cycle-consistent generative adversarial network.
4 . The method of claim 1 , wherein the scene type is indoor scene, outdoor scene, urban scene, rural scene, night scene, day scene, or a particular view of a particular location.
5 . The method of claim 1 , wherein the scene type is generic.
6 . The method of claim 1 , wherein visual characteristics include a distribution of textures or a distribution of colors.
7 . The method of claim 1 , wherein the first and second sets of training images both depict a similar distribution of object structures.
8 . A method for using a machine-learning model to identify objects depicted in real-domain sample images, wherein the machine learning model includes an object-recognition component and a real-to-synthetic-image component, and wherein the machine-learning model is associated with a mounted camera device, comprising:
by one or more image sensors of a mounted camera device, generating one or more real-domain sample images, the one or more real-domain sample images depicting the view of the mounted camera device;
at the mounted camera device, by the real-to-synthetic-image component, generating respective synthetic-appearing sample images based on the respective real-domain sample images;
at the mounted camera device, by the object-recognition component, identifying objects depicted in the synthetic-appearing sample images, wherein the object-recognition component was trained using a set of synthetic-domain image data; and
providing a report concerning the depicted objects based on the identification.
9 . The method of claim 8 , wherein the synthetic-appearing output images have visual characteristics that are similar to the visual characteristics associated with the set of synthetic-domain image data.
10 . The method of claim 8 , wherein the object-recognition component is a convolutional neural network.
11 . The method of claim 8 , wherein the real-to-synthetic-image component is a generative network of a cycle-consistent adversarial network.
12 . The method of claim 8 , wherein the mounted camera device is associated with a location, and the set of synthetic-domain image data represents objects and lighting conditions that are expected to be present at the location.
13 . The method of claim 8 , wherein the set of synthetic-domain image data used to train the object-recognition component was deterministically generated in accordance with a scene specification outline and a seed value, wherein the scene specification outline specifies a range of scenes, and wherein a scene comprises one or more objects and a camera model.