Results 11 to 20 of about 6,131,436 (285)
Unsupervised Learning for Solving the Travelling Salesman Problem [PDF]
Neural Information Processing Systems, 2023 We propose UTSP, an unsupervised learning (UL) framework for solving the Travelling Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss.
Yimeng Min, Yiwei Bai, Carla P. Gomes
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Generative Cooperative Learning for Unsupervised Video Anomaly Detection [PDF]
Computer Vision and Pattern Recognition, 2022 Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite sparse, likely because anomalies are less frequent in occurrence and usually ...
M. Zaheer +5 more
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Unsupervised Degradation Representation Learning for Blind Super-Resolution [PDF]
Computer Vision and Pattern Recognition, 2021 Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling).
Longguang Wang +6 more
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Momentum Contrast for Unsupervised Visual Representation Learning [PDF]
Computer Vision and Pattern Recognition, 2019 We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large
Kaiming He +4 more
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Unsupervised Cross-lingual Representation Learning at Scale [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks.
Alexis Conneau +9 more
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Towards Unsupervised Deep Graph Structure Learning [PDF]
The Web Conference, 2022 In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the original graph structures; besides, the ...
Yixin Liu +5 more
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Unsupervised Learning of Depth and Ego-Motion from Video [PDF]
Computer Vision and Pattern Recognition, 2017 We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use an end-to-end learning approach with view synthesis as the ...
Tinghui Zhou +3 more
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DetCo: Unsupervised Contrastive Learning for Object Detection [PDF]
IEEE International Conference on Computer Vision, 2021 We present DetCo, a simple yet effective self-supervised approach for object detection. Unsupervised pre-training methods have been recently designed for object detection, but they are usually deficient in image classification, or the opposite.
Enze Xie +6 more
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GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and egomotion estimation from videos. The three components are coupled by the nature of 3D scene geometry, jointly learned by our framework in an end-to-end ...
Zhichao Yin, Jianping Shi
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On the Philosophy of Unsupervised Learning
Philosophy & Technology, 2023 Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date.
D. Watson
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