We used this to train an AI model to identify the domain of each pixel, while updating the AI model to intentionally fail to identify the domain. Adversarial learning is a machine learning technique that trains a model by having it compete with another model that tries to deceive it. We also used adversarial learning to encourage AI models to recognize features common to both domains. In addition to simply replacing images, it also considers the position and existence probability of objects in the image by using the area information of the objects (cars, people, etc.) in the image to replace objects of the same type with each other (Fig. In this method, the domain gap is reduced by replacing parts of the image of the source domain and the image of the target domain. Therefore, to bridge the domain gap created when the target domain differs significantly from the source domain, we developed a new Few-shot Domain Adaptation algorithm with a data augmentation method that synthesizes multiple images. This leads to a deterioration in object detection accuracy. However, when the appearance of the source domain and target domain is significantly different, for example, with RGB images (source) and infrared images (target), the conventional method cannot fill the knowledge gap (domain gap) *3 between them. This approach is useful when labeled data in the target domain is scarce or expensive to obtain, since only a small amount of on-site data needs to be prepared. One of them is "Few-shot Domain Adaptation", which adapts the prior knowledge of an AI model trained on a large amount of publicly available labeled data (source domain data) to a different domain with only a few labeled examples (target domain). In order to solve this dilemma, technologies that realize high-performance AI models even with a small amount of data attract attention in the research community. To realize an accurate AI model, it is essential to prepare a large amount of training data through data collection and annotation, which requires a lot of time and money. We have been able to demonstrate the effectiveness of this method in object detection. Aiming to apply this technology to a wide range of businesses and solutions for the Panasonic Group, we are proceeding with demonstration experiments using a variety of field data. The newly developed Few-shot Domain Adaptation technology *2 enables the deployment of AI models to other sites with high accuracy, using significantly less training data than previously required, even for sites with significantly varied environments. For this reason, a great amount of time and money is required to prepare data when deploying developed AI to various sites with different environments, and the demand for technology that can reduce data preparation costs is increasing. In each case, it is necessary to acquire a large amount of training data and annotate objects in the image each time. A large amount of training data is required to develop an AI model capable of detecting objects such as people and cars from images accurately. In recent years, the implementation of AI has progressed in various fields such as public facilities and automobiles, supporting the safety and security of life and work. Osaka, Japan - Panasonic Holdings Corporation (hereinafter referred to as Panasonic HD) has developed a new technology that can reduce the cost of data preparation (collecting and annotating large amount of datasets) by half while suppressing the deterioration of object detection accuracy.
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