EAGER: Visual Representation Learning Using Mixed Labeled and Unlabeled Data

深度学习的最新进展在视觉识别和目标检测方面取得了巨大的成果。这些深度学习模型有各种各样的应用，从自动驾驶汽车到早期疾病诊断和家用机器人。然而，大多数这样的模型是受监督的，这意味着它们需要大规模的手动注释数据集来调整参数，并且在许多应用程序中获取注释可能是昂贵的。该项目探索了一系列自我监督学习算法，其中学习仅基于未标记的数据。新的模型可以学习视觉特征，这些特征可以用于各种视觉识别任务，包括物体检测和动作识别。该项目为代表性不足的群体提供研究机会，并将研究成果纳入课程课程。该项目研究了一系列自监督学习算法，这些算法可以从未标记的图像和视频中学习丰富的特征。自监督学习算法通过在自然图像或视频空间中建模一些规则来从未标记的数据中获取知识。该项目研究了一种新的自监督学习算法，该算法通过将图像的变换与其表示的变换相关联来约束学习。此外，本项目研究了一种新的多任务学习框架，用于聚合从多个监督和自监督学习算法中学习到的知识。该算法采用量化方法，忽略了知识传递过程中表征的任务特定细节。该算法产生了一组丰富的表示，可以很好地泛化各种视觉识别任务。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recent advances in deep learning has led to great results in visual recognition and object detection. These deep learning models have various applications from self-driving cars to early disease diagnosis and household robots. However, most such models are supervised, meaning that they need large scale manually annotated datasets to tune the parameters, and obtaining the annotation may be expensive in many applications. This project explores a family of self-supervised learning algorithms where the learning is based on unlabeled data only. The new models can learn visual features that can be used for various visual recognition tasks including object detection and action recognition. This project provides research opportunities for under-represented groups and integrates research outcomes into the course curriculum.This project studies a family of self-supervised learning algorithms that can learn rich features from unlabeled images and videos. Self-supervised learning algorithms harvest the knowledge from unlabeled data by modeling some regularity in the space of natural images or videos. This project studies novel self-supervised learning algorithms based on constraining the learning by relating transformations of images to transformations of their representations. Moreover, this project studies a novel multi-task learning framework for aggregating the knowledge learned from multiple supervised and self-supervised learning algorithms. This algorithm uses quantization methods to ignore the task specific details of the representation in transferring the knowledge. This algorithm results in a rich set of representations that generalize well across various visual recognition tasks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.