Feature learning

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  1. There are lots of awesome papers studying self-supervision for various tasks such as Image/Video Representation learning, Reinforcement learning, and Robotics.[1]
  2. Although deep learning has made some progress in data feature learning, it still faces many scientific challenges.[2]
  3. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos.[3]
  4. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.[3]
  5. We present a Function Feature Learning (FFL) method that can measure the similarity of non-convex neural networks.[4]
  6. This paper proposes a middle ground in which a deep neural architecture is employed for feature learning followed by traditional feature selection and classification.[5]
  7. There are two basic blocks in RSIR, including feature learning and similarity matching.[6]
  8. In this paper, we focus on developing an effective feature learning method for RSIR.[6]
  9. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm.[6]
  10. Furthermore, our proposed deep feature learning method can be adapted to both single-channel and multi-channel imaging data.[7]
  11. In our study, the minimum unit or a sample is a patch in the feature learning stage, rather than a whole brain image.[7]
  12. As for CNN-based classification, as CNN has an ability to combine feature learning and classification together, which directly generates the soft label as the final output from the neuronal network.[7]
  13. Since these features are from different domains, more advanced feature learning and integration methods need to be developed.[7]
  14. We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule.[8]
  15. Unsupervised feature learning may be used to provide initialized weights to the final supervised network, often more relevant than random ones (Bengio et al., 2007).[8]
  16. (2011) benchmarked four unsupervised feature learning methods (k-means, triangle k-means, RBM, and sparse auto-encoders) with only one layer.[8]
  17. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.[9]
  18. Supervised feature learning is learning features from labeled data.[9]
  19. Unsupervised feature learning is learning features from unlabeled data.[9]
  20. The goal of unsupervised feature learning is often to discover low-dimensional features that captures some structure underlying the high-dimensional input data.[9]
  21. In this paper, we review the development of data representation learning methods.[10]
  22. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models.[10]
  23. The history of data representation learning is introduced, while available online resources (e.g., courses, tutorials and books) and toolboxes are provided.[10]
  24. At the end, we give a few remarks on the development of data representation learning and suggest some interesting research directions in this area.[10]
  25. Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning.[11]
  26. In Self-taught learning and Unsupervised feature learning, we will give our algorithms a large amount of unlabeled data with which to learn a good feature representation of the input.[12]
  27. There are two common unsupervised feature learning settings, depending on what type of unlabeled data you have.[12]
  28. One extension to Unsupervised Feature Learning with Auto-encoders is De-noising Auto-encoders.[13]
  29. Dosovitskiy et al. propose a very interesting Unsupervised Feature Learning method that uses extreme data augmentation to create surrogate classes for unsupervised learning.[13]
  30. Thank you for reading this paper introducing Unsupervised Feature Learning![13]
  31. He describes deep learning in terms of the algorithms ability to discover and learn good representations using feature learning.[14]

소스

  1. ↑ jason718/game-feature-learning: Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery, CVPR'18
  2. ↑ Deep Feature Learning for Big Data
  3. ↑ 3.0 3.1 Reading Digits in Natural Images with Unsupervised Feature Learning – Google Research
  4. ↑ Function Feature Learning of Neural Networks
  5. ↑ Deep feature learning and selection for activity recognition
  6. ↑ 6.0 6.1 6.2 Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval
  7. ↑ 7.0 7.1 7.2 7.3 Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages
  8. ↑ 8.0 8.1 8.2 Unsupervised Feature Learning With Winner-Takes-All Based STDP
  9. ↑ 9.0 9.1 9.2 9.3 Feature learning
  10. ↑ 10.0 10.1 10.2 10.3 An overview on data representation learning: From traditional feature learning to recent deep learning
  11. ↑ Unsupervised Feature Learning and Deep Learning Tutorial
  12. ↑ 12.0 12.1 Unsupervised Feature Learning and Deep Learning Tutorial
  13. ↑ 13.0 13.1 13.2 Unsupervised Feature Learning
  14. ↑ What is Deep Learning?

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  • [{'LOWER': 'feature'}, {'LEMMA': 'learn'}]
  • [{'LOWER': 'representation'}, {'LEMMA': 'learning'}]
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