Into the wild: a study in rendered synthetic data and domain adaptation methods

2019 ◽  
Author(s):  
Marissa Dotter ◽  
Chelsea Mediavilla ◽  
Jonathan Sato ◽  
Chris M. Ward ◽  
Shibin Parameswaran ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Synthetic Data

scholarly journals Vision-Based Multirotor Following Using Synthetic Learning Techniques

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4794
Author(s):  
Alejandro Rodriguez-Ramos ◽  
Adrian Alvarez-Fernandez ◽  
Hriday Bavle ◽  
Pascual Campoy ◽  
Jonathan P. How
Keyword(s):  
Reinforcement Learning ◽  
Object Detection ◽  
Learning Strategies ◽  
Motion Control ◽  
Domain Adaptation ◽  
Control Strategies ◽  
Synthetic Data ◽  
Real Data ◽  
Stable Convergence ◽  
Learning Techniques

Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights).

Empirical Use of Information Retrieval to Build Synthetic Data for SMT Domain Adaptation

2016 ◽  
Vol 24 (4) ◽  
pp. 745-754 ◽  
Author(s):  
Sadaf Abdul-Rauf ◽  
Holger Schwenk ◽  
Patrik Lambert ◽  
Mohammad Nawaz
Keyword(s):  
Information Retrieval ◽  
Domain Adaptation ◽  
Synthetic Data

scholarly journals GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zane K. J. Hartley ◽  
Aaron S. Jackson ◽  
Michael Pound ◽  
Andrew P. French
Keyword(s):  
3D Reconstruction ◽  
Domain Adaptation ◽  
Synthetic Data ◽  
3D Models ◽  
Training Data ◽  
Plant Phenotyping ◽  
Size Estimation ◽  
Unsupervised Domain Adaptation ◽  
High Quality Image ◽  
Modelling Environment

3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.

scholarly journals Synthetic Source Universal Domain Adaptation through Contrastive Learning

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7539
Author(s):  
Jungchan Cho
Keyword(s):  
Domain Adaptation ◽  
Synthetic Data ◽  
Real Data ◽  
Target Domain ◽  
Source Domain ◽  
Universal Domain ◽  
Model Training ◽  
Using Data ◽  
Imaging Sensors ◽  
Target Data

Universal domain adaptation (UDA) is a crucial research topic for efficient deep learning model training using data from various imaging sensors. However, its development is affected by unlabeled target data. Moreover, the nonexistence of prior knowledge of the source and target domain makes it more challenging for UDA to train models. I hypothesize that the degradation of trained models in the target domain is caused by the lack of direct training loss to improve the discriminative power of the target domain data. As a result, the target data adapted to the source representations is biased toward the source domain. I found that the degradation was more pronounced when I used synthetic data for the source domain and real data for the target domain. In this paper, I propose a UDA method with target domain contrastive learning. The proposed method enables models to leverage synthetic data for the source domain and train the discriminativeness of target features in an unsupervised manner. In addition, the target domain feature extraction network is shared with the source domain classification task, preventing unnecessary computational growth. Extensive experimental results on VisDa-2017 and MNIST to SVHN demonstrated that the proposed method significantly outperforms the baseline by 2.7% and 5.1%, respectively.

scholarly journals Wildfire Smoke Classification Based on Synthetic Images and Pixel- and Feature-Level Domain Adaptation

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7785
Author(s):  
Jun Mao ◽  
Change Zheng ◽  
Jiyan Yin ◽  
Ye Tian ◽  
Wenbin Cui
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Synthetic Data ◽  
Real Data ◽  
Training Image ◽  
Classification Model ◽  
Environmental Diversity ◽  
Wildfire Smoke ◽  
Synthetic Images ◽  
Adaptation Method

Training a deep learning-based classification model for early wildfire smoke images requires a large amount of rich data. However, due to the episodic nature of fire events, it is difficult to obtain wildfire smoke image data, and most of the samples in public datasets suffer from a lack of diversity. To address these issues, a method using synthetic images to train a deep learning classification model for real wildfire smoke was proposed in this paper. Firstly, we constructed a synthetic dataset by simulating a large amount of morphologically rich smoke in 3D modeling software and rendering the virtual smoke against many virtual wildland background images with rich environmental diversity. Secondly, to better use the synthetic data to train a wildfire smoke image classifier, we applied both pixel-level domain adaptation and feature-level domain adaptation. The CycleGAN-based pixel-level domain adaptation method for image translation was employed. On top of this, the feature-level domain adaptation method incorporated ADDA with DeepCORAL was adopted to further reduce the domain shift between the synthetic and real data. The proposed method was evaluated and compared on a test set of real wildfire smoke and achieved an accuracy of 97.39%. The method is applicable to wildfire smoke classification tasks based on RGB single-frame images and would also contribute to training image classification models without sufficient data.

Text and Synthetic Data for Domain Adaptation in End-to-End Speech Recognition

2021 ◽  
pp. 271-278
Author(s):  
Juan Hussain ◽  
Christian Huber ◽  
Sebastian Stüker ◽  
Alexander Waibel
Keyword(s):  
Speech Recognition ◽  
Domain Adaptation ◽  
Synthetic Data ◽  
End To End

scholarly journals Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 583
Author(s):  
Gabriel Villalonga ◽  
Joost Van de Weijer ◽  
Antonio M. López
Keyword(s):  
Domain Adaptation ◽  
Synthetic Data ◽  
Recognition System ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sign Recognition ◽  
Short Period ◽  
Synthetic Images

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio ∼ 1 / 4 for new/known classes; even for more challenging ratios such as ∼ 4 / 1 , the results are also very positive.

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