Learning to Learn Visual Object Categories by Integrating Deep Learning with Hierarchical Bayes
Learning to Learn Visual Object Categories by Integrating Deep Learning with Hierarchical Bayes
复制标题
通过将深度学习与分层贝叶斯相结合来学习视觉对象类别
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Tenenbaum
中科院分区：
文献类型：
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作者：
Andres Campero;Andrew Francl;J. Tenenbaum
人类能够在很少的经验下概括和学习新的概念。他们有能力从他们获得的概念中创建语义结构，他们可以学习适当的归纳偏见，这些偏见后来被用作不同任务的先验，他们可以从很少的例子中学习新的类别。虽然神经网络和其他机器学习方法的最新进展开始在几个任务中接近人类水平的能力，但事实证明，建立复制这些能力的计算模型是困难的。我们提出了一种模型，该模型结合了从深度神经网络提取的强大特征和使用概率层次贝叶斯推断的语义结构。我们在三个不同的任务中测试和演示了我们的模型的能力：从一个新类别的单个示例学习新概念，从不同类别的少数几个示例学习新类别，以及从一组未标记的新对象学习语义树。
Humans are capable of generalizing and learning new concepts after very little experience. They have the ability to create semantic structures from concepts they acquire, they can learn appropriate inductive biases that are later used as priors for different tasks, and they can learn novel categories from very few examples. While recent advances in neural networks and other machine learning methods are beginning to approach humanlevel capabilities in several tasks, building computational models that replicate these abilities has proven difficult. We propose a model that combines powerful features extracted from a deep neural network with a semantic structure inferred using probabilistic Hierarchical Bayes. We test and demonstrate the capabilities of our model in three different tasks: learning a new concept from a single example of a novel category, learning new categories from few examples of different categories, and learning the semantic tree from an unlabeled set of novel objects.