9.7k citations · 26.5k across the 43 of their papers we have counts for
12 papers · 1 filter
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla +22
The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving dat…
RandAugment: Practical automated data augmentation with a reduced search space
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens +1
Recent work has shown that data augmentation has the potential to significantly improve the generalization of deep learning models. Recently, automated augmentation strategies have…
StarNet: Targeted Computation for Object Detection in Point Clouds
Jiquan Ngiam, Benjamin Caine, Wei Han +10
Detecting objects from LiDAR point clouds is an important component of self-driving car technology as LiDAR provides high resolution spatial information. Previous work on point-clo…
Learning Data Augmentation Strategies for Object Detection
Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi +3
Data augmentation is a critical component of training deep learning models. Although data augmentation has been shown to significantly improve image classification, its potential h…
Stand-Alone Self-Attention in Vision Models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani +3
Convolutions are a fundamental building block of modern computer vision systems. Recent approaches have argued for going beyond convolutions in order to capture long-range dependen…
Visual Wake Words Dataset
Aakanksha Chowdhery, Pete Warden, Jonathon Shlens +2
The emergence of Internet of Things (IoT) applications requires intelligence on the edge. Microcontrollers provide a low-cost compute platform to deploy intelligent IoT application…
Using learned optimizers to make models robust to input noise
Luke Metz, Niru Maheswaranathan, Jonathon Shlens +2
State-of-the art vision models can achieve superhuman performance on image classification tasks when testing and training data come from the same distribution. However, when models…
A Fourier Perspective on Model Robustness in Computer Vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens +2
Achieving robustness to distributional shift is a longstanding and challenging goal of computer vision. Data augmentation is a commonly used approach for improving robustness, howe…
A Learned Representation for Scalable Vector Graphics
Raphael Gontijo Lopes, David Ha, Douglas Eck +1
Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world. In spite of such…
Using Videos to Evaluate Image Model Robustness
Keren Gu, Brandon Yang, Jiquan Ngiam +2
Human visual systems are robust to a wide range of image transformations that are challenging for artificial networks. We present the first study of image model robustness to the m…
Attention Augmented Convolutional Networks
Irwan Bello, Barret Zoph, Ashish Vaswani +2
Convolutional networks have been the paradigm of choice in many computer vision applications. The convolution operation however has a significant weakness in that it only operates…
Accelerating Training of Deep Neural Networks with a Standardization Loss
Jasmine Collins, Johannes Balle, Jonathon Shlens
A significant advance in accelerating neural network training has been the development of normalization methods, permitting the training of deep models both faster and with better…