Click the “CVer” above and select “Star” to pin it Heavyweight content delivered first If we were to rank several stages in the deep learning development process by importance, preparing training data would surely be among the top few. It’s important to understand that once a model network is written, it is merely a chunk … Read more
1 Compiled by New Intelligence Source: blog.kaggle.com Compiled by: Jia Yuepeng [New Intelligence Guide]The champion team of the Kaggle Ocean Fish Recognition and Classification Competition shares their technology: How to design robust optimization algorithms? How to analyze data and perform data augmentation? Technical details include using images from different boats for validation and how to … Read more
The powerful feature representation and nonlinear fitting capability of deep neural networks stem from sufficient learning on high-quality datasets. However, in practical engineering applications, due to economic and labor costs, acquiring a large amount of typical labeled data becomes extremely challenging, resulting in avery limited number of training samples. Data augmentation methods provide a simple … Read more
Machine Heart Column Author: Zhang Junlin BERT has brought great surprises to people, but in the blink of an eye, about half a year has passed, and during this time, many new works related to BERT have emerged. In recent months, aside from my main work on recommendation algorithms, I have been quite curious about … Read more
Article Author: Zhang Junlin, Senior Algorithm Expert at Weibo AI Lab Content Source: Deep Learning Frontier Notes @ Zhihu Column Community Production: DataFun Note: You are welcome to leave messages in the background to submit to DataFun. BERT has brought great surprises to people, but it has been about half a year since then, and … Read more
【Introduction】The traditional visual recognition methods struggle to directly distinguish between NIs (Natural Images) and CG (Computer Generated images). This article proposes an efficient image recognition method based on Convolutional Neural Networks (CNNs). A large number of experiments were conducted to evaluate the model’s performance. The experimental results show that this method outperforms existing recognition methods … Read more