Abstract: This disclosure describes utilizing an image model protection system to improve the defensive robustness of a large generative image model against the generation of harmful digital images. For example, the image model protection system uses digital signatures of identified harmful images to determine whether a particular harmful image was generated by a specific large generative image model. Using digital signatures, the image model protection system matches the harmful image to images generated by the large generative image model. The image model protection system then identifies the prompt used to generate the image at the large generative image model. Furthermore, the image model protection system uses the harmful prompt to implement new security measures to safeguard the large generative image model against the generation of similar harmful images in the future.

Abstract: This disclosure describes utilizing an image model protection system to improve the defensive robustness of a large generative image model against the generation of harmful digital images. For example, the image model protection system uses digital signatures of identified harmful images to determine whether a particular harmful image was generated by a specific large generative image model. Using digital signatures, the image model protection system matches the harmful image to images generated by the large generative image model. The image model protection system then identifies the prompt used to generate the image at the large generative image model. Furthermore, the image model protection system uses the harmful prompt to implement new security measures to safeguard the large generative image model against the generation of similar harmful images in the future.

Abstract: This disclosure describes utilizing an image model protection system to improve the defensive robustness of a large generative image model against the generation of harmful digital images. For example, the image model protection system uses digital signatures of identified harmful images to determine whether a particular harmful image was generated by a specific large generative image model. Using digital signatures, the image model protection system matches the harmful image to images generated by the large generative image model. The image model protection system then identifies the prompt used to generate the image at the large generative image model. Furthermore, the image model protection system uses the harmful prompt to implement new security measures to safeguard the large generative image model against the generation of similar harmful images in the future.

Abstract: A computer-implemented technique generates a dense embedding vector that provides a distributed representation of input text. The technique includes: generating an input term-frequency (TF) vector of dimension g that includes frequency information relating to frequency of occurrence of terms in an instance of input text; using a TF-modifying component to modify the term-specific frequency information in the input TF vector by respective machine-trained weighting factors, to produce an intermediate vector of dimension g; using a projection component to project the intermediate vector of dimension g into an embedding vector of dimension k, where k is less than g. Both the TF-modifying component and the projection component use respective machine-trained neural networks. An application performs any of a retrieval-based function, a recognition-based function, a recommendation-based function, a classification-based function, etc. based on the embedding vector.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.

Abstract: A computer-implemented technique is described herein for generating a dense embedding vector that provides a distribution representation of input text. In one implementation, the technique includes: generating an input term-frequency (TF) vector of dimension g that includes frequency information relating to frequency of occurrence of terms in an instance of input text; using a TF-modifying to modify the term-specific frequency information in the input TF vector by respective machine-trained weighting factors, to produce an intermediate vector of dimension g; using a projection component to project the intermediate vector of dimension g into an embedding vector of dimension k, where k is less than g. Both the TF-modifying component and the projection component can use respective machine-trained neural networks. An application component can perform any of a retrieval-based function, a recognition-based function, a recommendation-based function, a classification-based function, etc.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.