我们的研究目标是开发促进自动视觉识别的方法。为了预测与图像相关的唯一或多重标签，我们研究了用于监督特征学习的不同类型的深度神经网络结构和方法。我们首先回顾了卷积神经网络的最新进展，旨在了解这一系列统计模型背后的历史、现代结构的局限性以及当前用于训练深层CNN的新技术。我们工作的独创性在于我们专注于低数据量任务处理的方法。我们采用不同的模型和技术在几种数据集上获得最佳精度，例如用于构建网页API的中等食物食谱数据集（100k图像）或用于DSG在线挑战的小规模卫星图片（6000张）。我们还拟定了弱监督学习的最新技术，引入了能够定位感兴趣区域的不同类型的CNN。我们的最后一个贡献是建立在Torch7之上的框架，用于在任何视觉识别任务和任何规模的数据集上训练和测试深度模型。
The goal of our research is to develop methods advancing automaticvisual recognition. In order to predict the unique or multiple labelsassociated to an image, we study different kind of Deep Neural Networksarchitectures and methods for supervised features learning. We first draw up astate-of-the-art review of the Convolutional Neural Networks aiming tounderstand the history behind this family of statistical models, the limit ofmodern architectures and the novel techniques currently used to train deepCNNs. The originality of our work lies in our approach focusing on tasks with alow amount of data. We introduce different models and techniques to achieve thebest accuracy on several kind of datasets, such as a medium dataset of foodrecipes (100k images) for building a web API, or a small dataset of satelliteimages (6,000) for the DSG online challenge that we’ve won. We also draw up thestate-of-the-art in Weakly Supervised Learning, introducing different kind of CNNsable to localize regions of interest. Our last contribution is a framework,build on top of Torch7, for training and testing deep models on any visualrecognition tasks and on datasets of any scale.
1 卷积神经网络
2 深度CNN的迁移学习
3 弱监督学习
附录A Overfeat
附录B Vgg16
附录C InceptionV3
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