# US 2025/0209796 A1 · Generating annotated data samples for training using trained generative model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0209796 A1 - Document ID: US20250209796A1 - Status: Published application - Kind: A1 - Application number: 18/394,509 - Filed: 2023-12-22 - Published: 2025-06-26 - Earliest claimed: 2023-12-22 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Markus POBITZER; Filip Michal JANICKI; Mattia RIGOTTI; Adelmo Cristiano Innocenza MALOSSI - Web page: https://patentgrep.com/patent/US20250209796A1 ## CPC - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06T 5/20: using local operators - G06T 5/60: using machine learning, e.g. neural networks - G06T 5/77: Retouching; Inpainting; Scratch removal - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V 10/82: using neural networks - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract An example system includes a processor to receive an annotated data sample comprising an object contained in an annotated mask. The processor can partially erase the object contained in the annotated mask. The processor can fill out an erased area of the object a predetermined number of times via a generative model to generate additional annotated data samples. ## Claims ### Claim 1 A system, comprising a processor to: receive an annotated data sample comprising an object contained in an annotated mask; partially erase the object contained in the annotated mask; and fill out an erased area of the object a predetermined number of times via a trained generative model to generate an additional annotated data sample. ### Claim 2 (depends on 1) The system of claim 1, wherein the trained generative model comprises a diffusion-based inpainting model. ### Claim 3 (depends on 1) The system of claim 1, wherein the generative model comprises an inpainting model trained on top of a latent diffusion model. ### Claim 4 (depends on 1) The system of claim 1, wherein the generated additional annotated data sample comprises an image-mask pair that comprises the annotated mask of the annotated data sample. ### Claim 5 (depends on 1) The system of claim 1, wherein the processor is to erode the annotated mask to generate an eroded mask and erase an area of the object within the eroded mask to partially erase the object. ### Claim 6 (depends on 1) The system of claim 1, wherein the processor is to blend an edge of the filled out area of the object with an original outer portion of the object that was not erased using a Gaussian filter. ### Claim 7 (depends on 1) The system of claim 1, wherein the processor is to input the associated class from the annotated mask as a text guidance into the diffusion-based inpainting model. ### Claim 8 A computer-implemented method, comprising: receiving, via a processor, an annotated data sample comprising an object contained in an annotated mask; partially erasing, via the processor, the object contained in the annotated mask; and filling out, via the processor, an erased area of the object via a trained generative model to generate an additional annotated data sample. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, further comprising training a segmentation learning model using the generated additional annotated data sample. ### Claim 10 (depends on 8) The computer-implemented method of claim 8, wherein the generated additional annotated data sample comprises the annotated mask from the annotated data sample. ### Claim 11 (depends on 8) The computer-implemented method of claim 8, further comprising receiving a text prompt and filling out the erased area using a diffusion-based inpainting model guided by the text prompt. ### Claim 12 (depends on 8) The computer-implemented method of claim 8, wherein partially erasing the object comprises eroding the annotated mask to generate an eroded mask and erasing an area of the object within the eroded mask. ### Claim 13 (depends on 8) The computer-implemented method of claim 8, wherein filling out the erased area comprises blending an edge of the filled out area of the object with an original outer portion of the object that was not erased using a Gaussian filter. ### Claim 14 (depends on 8) The computer-implemented method of claim 8, further comprising filling out, via the processor, the erased area of the object via the generative model a predetermined number of times to generate a plurality of additional annotated data samples having the same annotation as the annotated data sample. ### Claim 15 A computer program product for generation of annotated data samples, the computer program product comprising a computer-readable storage medium having program code embodied therewith, the program code executable by a processor to cause the processor to: receive an annotated data sample comprising an object contained in an annotated mask; partially erase the object contained in the annotated mask; and fill out an erased area of the object a predetermined number of times via a generative model to generate additional annotated data samples. ### Claim 16 (depends on 15) The computer program product of claim 15, further comprising program code executable by the processor to train a segmentation learning model using the generated additional annotated data samples. ### Claim 17 (depends on 15) The computer program product of claim 15, further comprising program code executable by the processor to receive a text prompt and fill out the erased area using a diffusion-based inpainting model guided by the text prompt. ### Claim 18 (depends on 15) The computer program product of claim 15, further comprising program code executable by the processor to erode the annotated mask to generate an eroded mask and erase an area of the object within the eroded mask. ### Claim 19 (depends on 15) The computer program product of claim 15, further comprising program code executable by the processor to receive an erosion kernel and erode the annotated mask based on the erosion kernel. ### Claim 20 (depends on 15) The computer program product of claim 15, further comprising program code executable by the processor to fill the erased area of the object via the generative model a predetermined number of times to generate a plurality of additional annotated data samples having the same annotation as the annotated data sample.