A Survey of Deep Active Learning

🤖 Plain-English Summary

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. In addition, we also analyze and summarize the development of DeepAL from an application perspective.

🔑 Key Findings

• Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features.
• In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data.
• As a result, DL has attracted significant attention from researchers and has been rapidly developed.

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