# US 2021/0241030 A1 · Domain generation via learned partial domain translations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241030 A1 - Document ID: US20210241030A1 - Status: Published application - Kind: A1 - Application number: 16/777,017 - Filed: 2020-01-30 - Published: 2021-08-05 - Earliest claimed: 2020-01-30 - Assignees: Ford Global Technologies, LLC - Applicants: Ford Global Technologies, LLC - Inventors: Akhil Perincherry; Christopher Cruise - Web page: https://patentgrep.com/patent/US20210241030A1 ## CPC - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/092: Reinforcement learning - G06N 3/094: Adversarial learning - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/82: using neural networks - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system and a method can receive a first dataset having a first label and a first context. The system and the method can also generate, at the trained deep neural network, a second dataset having the first label and a second context according to a mapping, wherein a first mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having the first label and the first context to data having a second label and the first context and a second mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having a second label and the first context to data having the second label and the second context, wherein the second context is different from the first context and the second label is different from the first label. ## Claims ### Claim 1 A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: receive, at a trained deep neural network, a first dataset having a first label and a first context; and generate, at the trained deep neural network, a second dataset having the first label and a second context according to a plurality of mappings, wherein a first mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having the first label and the first context to data having a second label and the first context and a second mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having a second label and the first context to data having the second label and the second context, wherein the second context is different from the first context and the second label is different from the first label. ### Claim 2 (depends on 1) The system of claim 1, wherein the trained deep neural network comprises a generator trained within an adversarial neural network. ### Claim 3 (depends on 1) The system of claim 1, wherein the first dataset and the second dataset each comprise a plurality of image frames. ### Claim 4 (depends on 3) The system of claim 3, wherein the plurality of image frames corresponding to the first dataset comprise image frames captured by at least one of a forward-looking camera or a backward-looking camera. ### Claim 5 (depends on 4) The system of claim 4, wherein the plurality of image frames corresponding to the second dataset comprise generated image frames represented as being captured by the other of the forward-looking camera or the backward-looking camera. ### Claim 6 (depends on 5) The system of claim 5, wherein the first label corresponds to a forward-looking camera perspective and the second label corresponds to a backward-looking camera perspective. ### Claim 7 (depends on 6) The system of claim 6, wherein the first context corresponds to an illuminated environment and the second context corresponds to a non-illuminated environment. ### Claim 8 A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: receive, at a trained deep neural network, a first image having a first label representing a perspective of the first image and a first context representing an environment of the first image; and generate, via a plurality of mapping of the trained deep neural network, a second image having the first label and a second context representing an environment of the second image, wherein a first mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having the first label and the first context to data having a second label and the first context and a second mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having a second label and the first context to data having the second label and the second context, wherein the second context is different from the first context and the second label is different from the first label. ### Claim 9 (depends on 8) The system of claim 8, wherein the trained deep neural network comprises a generator trained within an adversarial neural network. ### Claim 10 (depends on 8) The system of claim 8, wherein the second image comprises a generated image having at least one of a forward-looking camera or a backward-looking camera perspective. ### Claim 11 (depends on 8) The system of claim 8, wherein the first context corresponds to an illuminated environment and the second context corresponds to a non-illuminated environment. ### Claim 12 (depends on 8) The system of claim 8, wherein the first context comprises at least one of a presence or an absence of a lane. ### Claim 13 A method comprising: receiving, at a trained deep neural network, a first dataset having a first label and a first context; and generating, at the trained deep neural network, a second dataset having the first label and a second context according to a plurality of mapping, wherein a first mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having the first label and the first context to data having a second label and the first context and a second mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having a second label and the first context to data having the second label and the second context, wherein the second context is different from the first context and the second label is different from the first label. ### Claim 14 (depends on 13) The method of claim 13, wherein the trained deep neural network comprises a generator trained within an adversarial neural network. ### Claim 15 (depends on 13) The method of claim 13, wherein the first dataset and the second dataset each comprise a plurality of image frames. ### Claim 16 (depends on 15) The method of claim 15, wherein the plurality of image frames corresponding to the first dataset comprise image frames captured by at least one of a forward-looking camera or a backward-looking camera. ### Claim 17 (depends on 16) The method of claim 16, wherein the plurality of image frames corresponding to the second dataset comprise generated image frames represented as being captured by the other of the forward-looking camera or the backward-looking camera. ### Claim 18 (depends on 17) The method of claim 17, wherein the first label corresponds to a forward-looking camera perspective and the second label corresponds to a backward-looking camera perspective. ### Claim 19 (depends on 18) The method of claim 18, wherein the first context corresponds to an illuminated environment and the second context corresponds to a non-illuminated environment. ### Claim 20 (depends on 13) The method of claim 13, wherein the first context comprises at least one of a presence or an absence of a lane.