Promoting DX through AI utilization from a small amount of data.
How to break free from 'You can't shake a sleeve that doesn't exist'! There are countermeasures even if you have limited data.
We support measures for those who want to utilize AI for DX promotion but are struggling with a lack of data. By leveraging publicly available image and label datasets, we explore the similarities with your existing data, enabling discrimination even for items not present in your data. If your data has sufficient quality and quantity, we will propose analyses tailored to your specific challenges. 【Measures for Insufficient Data (Partial)】 <Innovating Data> ■ Efforts to collect more data ■ Performing Data Augmentation It is also possible to virtually add data through simulation. *For more details, please refer to the PDF materials or feel free to contact us.
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【Measures to Take When Other Data is Insufficient】 <Innovating the Model> ■ Adding appropriate features or splitting the model often improves accuracy. ■ Adjusting the loss function that evaluates the discrepancy between the correct answer and the estimated value can sometimes lead to effective learning. ■ Utilizing a pre-trained model for tuning can significantly reduce the amount of data needed for training. *For more details, please refer to the PDF document or feel free to contact us.
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For more details, please refer to the PDF document or feel free to contact us.
