Unsupervised Pre-Training of Image Features on Non-Curated Data論文研讀
Abstract 目前大多數非監督的特徵學習的數據集一般都是小且經過精挑細選的,比如說ImageNet,因爲評估transfer task的時候使用non-curated的原數據會降低特徵的質量. 這篇文章的目的就是去研究在non-curated的數據下的非監督學習的方式.針對這一問題，提出了一種新的無監督方法，即利用自監督和聚類的方法來獲取大規模數據中的complementary statist
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相關文章
- 1. 論文閱讀 《Multimodal Remote Sensing Image Registration Based on Image Transfer and Local Features》
- 2. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
- 3. 《論文閱讀》UCTGAN: Diverse Image Inpainting based on Unsupervised Cross-Space Translation
- 4. Sequential modeling of deep features for breast cancer histopathological image classification 論文閱讀
- 5. Unsupervised Learning of Edges 論文閱讀
- 6. Unsupervised Data Augmentation For Consistency Training 論文閱讀
- 7. 論文Multimodal Unsupervised Image-to-Image Translation
- 8. Unpaired/Partially/Unsupervised Image Captioning
- 9. 【論文筆記】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 10. Bags of Local Convolutional Features for Scalable Instance Search 論文解讀