RI:Small: Unsupervised Discriminatively-Generative Learning:

通过监督训练获得强大的判别分类器已经取得了巨大的成功，其中人类对训练数据提供手动注释。无监督学习，其中输入数据不附带特定于任务的注释，是非常重要的，因为大量的任务没有监督或很少监督。然而，它仍然是机器学习中最困难的问题之一。典型的无监督学习任务为高度结构化的数据（如图像、视频、语音和文本）学习有效的生成表示。现有的无监督学习的生成模型往往受到其简化假设的限制，而现有的有监督学习的判别模型的生成能力有限。该项目开发了一个新的内省机器学习框架，大大增强和扩展了单个模型的生成和识别能力。该项目的成果，内省生成/判别学习，通过为计算机视觉，机器学习，认知科学，计算语言学和数据挖掘等广泛领域建立更强大的计算模型，显着提高了现有算法的学习能力。本研究探讨了一种新的机器学习框架，内省生成/判别学习（IGDL），它实现了一个能够同时执行生成和分类的生成器/判别器。 IGDL生成器本身就是一个自学习器，能够进行自省--能够自我评估其生成的样本与给定训练数据之间的差异。当随后进行迭代判别学习时，诸如卷积神经网络（CNN）之类的现代判别分类器的期望属性可以直接由IGDL生成器继承。此外，IGDL的可扩展性方面还通过使用自我生成的新数据（称为伪否定）来增强对抗性样本的分类性能，从而在完全监督分类中产生有竞争力的结果。IGDL的训练过程是通过有效的反向传播使用两步分类合成算法进行的。研究了用于IGDL训练的有效随机梯度下降蒙特卡罗抽样过程。在机器学习的三个关键领域，包括无监督、半监督和全监督学习，IGDL在包括纹理合成、对象建模和图像分类在内的广泛应用中产生了有竞争力的结果。

Great success has been achieved in obtaining powerful discriminative classifiers via supervised training where humans provide manual annotations to the training data. Unsupervised learning, in which the input data is not accompanied with task-specific annotations, is of great importance since a large number of tasks have no to little supervision. However, it still remains to be one of the most difficult problems in machine learning. A typical unsupervised learning task learns effective generative representations for highly structured data such as images, videos, speech, and text. Existing generative models for unsupervised learning are often constrained by their simplified assumptions, while existing discriminative models for supervised learning are of limited generation capabilities. This project develops a new introspective machine learning framework that greatly enhances and expands the power of both generation and discrimination for a single model. The outcome of the project, introspective generative/discriminative learning, significantly improves the learning capabilities of the existing algorithms by building stronger computational models for a wide range of fields including computer vision, machine learning, cognitive science, computational linguistics, and data mining. This research investigates a new machine learning framework, introspective generative/discriminative learning (IGDL), which attains a single generator/discriminator capable of performing both generation and classification. The IGDL generator is itself a discriminator, capable of introspection --- being able to self-evaluate the difference between its generated samples and the given training data. When followed by iterative discriminative learning, desirable properties of modern discriminative classifiers such as convolutional neural networks (CNN) can be directly inherited by the IGDL generator. Moreover, the discriminator aspect of IGDL also produces competitive results in fully supervised classification by using self-generated new data (called pseudo-negatives) to enhance the classification performance against adversarial samples. The training process of IGDL is carried out using a two-step synthesis-by-classification algorithm via efficient backpropagation. Effective stochastic gradient descent Monte Carlo sampling processes for IGDL training are studied. Across three key areas in machine learning including unsupervised, semi-supervised, and fully-supervised learning, IGDL produces competitive results in a wide range of applications including texture synthesis, object modeling, and image classification.