5results about How to "Increase training data" patented technology

This invention belongs to the field of intelligent agent visual navigation technology. It discloses an image comparison and recognition method and device based on reinforcement learning, including the following steps: (1) designing the target recognition process as an MDP problem; (2) designing a domain randomization method that integrates the reservoir sampling method to sample multiple feature values ​​after randomization of the environmental feature domain; (3) gradually increasing environmental variable factors and task difficulty; (4) using a reward function to evaluate the strategy generated by the agent; (5) designing an experience replay mechanism and setting an experience buffer; (6) using a random sampling method for actions to enable the agent to interact with the environment to obtain a large amount of initial experience, while using a network loss experience replay mechanism to filter the experience in the experience pool for learning; (7) dynamically adjusting the exploratory desire coefficient in the agent's learning process, thereby realizing image comparison and recognition. This invention improves the generalization ability of target recognition.

A formula-based numerical reasoning question and answer implementation method, device and medium, the question and answer in the numerical reasoning question and answer application scene are obtained through formula knowledge annotation to obtain formula data set and parameter data set, which are respectively used for training of formula generation model and parameter identification model, for new questions in the question and answer application scene, formula information is generated by the formula generation model, the parameter name in the formula information is input into the parameter identification model, the parameter information of the formula is obtained, the formula information and the parameter information are input into the formula calculator, and the answer of the question is output. The construction of the training data can be automatically carried out, the data migration generalization and data expansion are facilitated; the formula calculator of the calculable unit is introduced in the calculation, the problem that the generative language model is not sensitive to the numerical value is avoided; the numerical reasoning is divided into several subtasks, the requirement for the model is reduced, the obtained answer has good explainability, and the modularization decoupling is favorable for model optimization.

The embodiment of the present application provides an image acquisition device, method and related equipment, and relates to the technical field of image acquisition, so as to solve the problem that the image restoration precision of the current lens-free imaging technology is poor, the array density of the photosensitive device in the photosensitive device is improved, which leads to the increase of the surface area of the photosensitive device, the increase of the manufacturing cost, and the color sensitivity of the small volume target object is not obviously enhanced, thereby affecting the practicability and convenience of the lens-free acquisition technology. The device comprises: at least two photosensitive device layers, wherein at least one of the photosensitive device layers close to the target object is used for light transmission, the photosensitive device layer comprises at least one photosensitive device, and the at least two photosensitive layers are stacked; each photosensitive device layer is used for acquiring a group of image data of the target object.

The application discloses a pathological image color restoration method and a scanner based on deep learning, and the method comprises the following steps: reducing the resolution of input data and target data based on a bilateral grid downsampling technology; training a neural network model for the first time by using the input data and the target data after the resolution is reduced; improving the resolution of the input data and the target data after the resolution is reduced based on a bilateral grid upsampling technology; and training the neural network model for the second time by using the input data and the target data after the resolution is improved, so that the training speed of the neural network model is improved, and the staining effect of the pathological image collected by a digital pathology scanner is improved.

The application relates to the technical field of natural language processing, in particular to a precise question and answer data enhancement method and system based on knowledge graph annotation, which comprises the following steps: firstly, a layered medical knowledge graph containing a type layer and an instance layer is constructed; then, a medical named entity recognition model is used to perform graph annotation on seed question and answer pairs, so that structured graph query modes are generated; next, entity replacement deformation and relationship path deformation are performed on the knowledge graph, so that multiple semantically equivalent derived query modes are generated; then, the natural language generator is used to convert the derived query modes into natural language question and answer pairs; finally, confidence filtering is performed based on attribute range constraints, relationship mutual exclusion constraints and path legality constraints. The application can automatically generate precise question and answer pairs with logical rigor and diverse content under the premise of maintaining medical semantic accuracy, effectively expand medical question and answer training data, and significantly reduce manual annotation costs.