3.9k citations · 17.4k across the 35 of their papers we have counts for
15 papers · 1 filter
Multi-Task Self-Training for Learning General Representations
Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk +2
Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision.…
Simple Training Strategies and Model Scaling for Object Detection
Xianzhi Du, Barret Zoph, Wei-Chih Hung +1
The speed-accuracy Pareto curve of object detection systems have advanced through a combination of better model architectures, training and inference methods. In this paper, we met…
Revisiting ResNets: Improved Training and Scaling Strategies
Irwan Bello, William Fedus, Xianzhi Du +5
Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scal…
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas +5
Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a…
Does Data Augmentation Benefit from Split BatchNorms
Amil Merchant, Barret Zoph, Ekin Dogus Cubuk
Data augmentation has emerged as a powerful technique for improving the performance of deep neural networks and led to state-of-the-art results in computer vision. However, state-o…
Rethinking Pre-training and Self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin +4
Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet pre-training is commonly used to initialize the backbones of object detection and segmentat…
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng +5
Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for ach…
Improving 3D Object Detection through Progressive Population Based Augmentation
Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk +10
Data augmentation has been widely adopted for object detection in 3D point clouds. However, all previous related efforts have focused on manually designing specific data augmentati…
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…
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…
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…
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu +5
The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much wo…
AutoAugment: Learning Augmentation Policies from Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane +2
Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In thi…
Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Maxim Neumann +7
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learni…
Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens +1
Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directl…