Dataset Abuse Is Rife in Computer Vision

Feb 10, 2023 · chatgpt ·

Data set abuse is a growing issue in computer vision. In recent years, the proliferation of public datasets and the ease of downloading and using them has enabled a large number of developers to create new computer vision algorithms and models. However, this convenience has also led to over-reliance on some datasets, with researchers reusing existing datasets multiple times and failing to collect their own data or verify the accuracy of existing datasets. This has caused problems ranging from poor generalization of models to implicit bias in results.

In response to this abuse, the research community has proposed various solutions. The most common approaches involve reducing the amount of lag time between the release of datasets and when they become publicly available, encouraging data curation and wider sharing of datasets, and providing more incentives for data released into the public domain. Additionally, researchers are exploring the use of newer techniques such as federated learning and synthetic data to increase data privacy and reduce the need for large amounts of labeled data.

Data set abuse can also be addressed at an organizational level. Companies need to prioritize data collection, verification and labeling processes to ensure high quality datasets are being released. They should also encourage the use of best practices in machine learning, such as proper validation testing, to avoid issues with over-fitting or bias in results. Finally, companies should provide adequate resources for data curation and labeling, as this is often a key bottleneck in the development of advanced computer vision models.

In conclusion, data set abuse is a growing problem in computer vision and organizations need to take action to address it. Solutions range from better data curation and verification to the use of newer techniques such as federated learning and synthetic data. Companies need to prioritize these efforts in order to ensure that computer vision models are accurate, unbiased, and applicable to real world scenarios.