convolutional neural networks - Is it difficult to learn the rotated bounding box for a (rotated) object? - Artificial Intelligence Stack Exchange
GitHub - fbuchert/rotation-prediction-pytorch: PyTorch implementation of Unsupervised Representation Learning by Predicting Image Rotations
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PDF] Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction. | Semantic Scholar
Part-4 Understanding Rotation Net approach for unsupervised representation learning - YouTube
ICML 2021 Paper: What Does Rotation Prediction Tell Us about Classifier Accuracy? - YouTube
The Illustrated PIRL: Pretext-Invariant Representation Learning
2020 Prediction: The Death of the Password Rotation Policy | SpyCloud Blog
Rotation Prediction Based Representative View Locating Framework for 3D Object Recognition - ScienceDirect
![PDF] Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction. | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/f7fa3b8ed5f3f75ace4fe8447ccd9abfbb19e621/1-Figure1-1.png "PDF] Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction. | Semantic Scholar")
PDF] Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction. | Semantic Scholar
Weijian Deng, Stephen Gould, Liang Zheng · What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? · SlidesLive
Ash Pokemon Journeys team rotation prediction by ChrisMaley0 on DeviantArt
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
Real-time neural network prediction for handling two-hands mutual occlusions - ScienceDirect
Etd | Evaluation of mandibular growth rotation prediction methods based on lateral cephalograpm measurements | ID: vq27zn61z | Digital Collections
![PDF] Impacts of Earth rotation parameters on GNSS ultra-rapid orbit prediction: Derivation and real-time correction by Qianxin Wang, Chao Hu, Tianhe Xu, Guobin Chang, Alberto Hernández Moraleda · 10.1016/j.asr.2017.09.022 · OA.mg](https://og.oa.mg/Impacts%20of%20Earth%20rotation%20parameters%20on%20GNSS%20ultra-rapid%20orbit%20prediction%3A%20Derivation%20and%20real-time%20correction.png?author=%20Qianxin%20Wang,%20Chao%20Hu,%20Tianhe%20Xu,%20Guobin%20Chang,%20Alberto%20Hern%C3%A1ndez%20Moraleda "PDF] Impacts of Earth rotation parameters on GNSS ultra-rapid orbit prediction: Derivation and real-time correction by Qianxin Wang, Chao Hu, Tianhe Xu, Guobin Chang, Alberto Hernández Moraleda · 10.1016/j.asr.2017.09.022 · OA.mg")
PDF] Impacts of Earth rotation parameters on GNSS ultra-rapid orbit prediction: Derivation and real-time correction by Qianxin Wang, Chao Hu, Tianhe Xu, Guobin Chang, Alberto Hernández Moraleda · 10.1016/j.asr.2017.09.022 · OA.mg
RED-NN: Rotation-Equivariant Deep Neural Network for Classification and Prediction of Rotation
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
5 Types of Machine Learning Algorithms With Use Cases
When Does Self-supervision Improve Few-shot Learning?
![PDF] What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/b0c847a76f4eae7da4ce9341fe03d76f5b1a30fb/3-Figure1-1.png "PDF] What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? | Semantic Scholar")
PDF] What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? | Semantic Scholar
Mathematics | Free Full-Text | Developing a Spine Internal Rotation Angle Measurement System Based Machine Learning Using CT Reconstructed X-ray Anteroposterior Image
GitHub - philiptheother/FeatureDecoupling: Self-Supervised Representation Learning by Rotation Feature Decoupling
Review — RotNet: Unsupervised Representation Learning by Predicting Image Rotations | by Sik-Ho Tsang | Medium
Growth rotation
Self-supervised features learning by rotation prediction [Gidaris et... | Download Scientific Diagram
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1911.07072] Unsupervised Deep Metric Learning via Auxiliary Rotation Loss
A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems | Nature Machine Intelligence
