| Autor: |
Liu, Junyu, Li, Xiang, Wang, Jin, Zhou, Jiqiang, Shen, Jianxiong |
| Rok vydání: |
2019 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Recent breakthroughs made by deep learning rely heavily on large number of annotated samples. To overcome this shortcoming, active learning is a possible solution. Beside the previous active learning algorithms that only adopted information after training, we propose a new class of method based on the information during training, named sequential-based method. An specific criterion of active learning called prediction stability is proposed to prove the feasibility of sequential-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability is effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperforms them on CIFAR-100. |
| Databáze: |
arXiv |
| Externí odkaz: |
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