Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain 1. 論文摘要 依據過去語音片段預測未來片段的CPC方法被證明是一種有效的表徵學習方法，本文作者在CPC算法模型的基礎上， 通過對過去語音片段在時間域上的數據增強（WavAugment) 取得了比其他方法更高效、更好的表徵效果。通過pa
相關文章
- 1. A Simple Framework for Contrastive Learning of Visual Representations
- 2. 論文寫作解讀：A Simple Framework for Contrastive Learning of Visual Representations
- 3. Article Analysis(AA): A Simple Framework for Contrastive Learning of Visual Representations
- 4. Deep Temporal Clustering: Fully unsupervised learning of time-domain features
- 5. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations學習與理解
- 6. Exploring the teaching of deep learning in neural networks
- 7. [ICML19] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
- 8. Contrastive Learning Of Structured World Models
- 9. The Rise of Meta Learning
- 10. 《A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling》筆記
- 更多相關文章...
- • PHP time() 函數 - PHP參考手冊
- • SQL IN 操作符 - SQL 教程
- • JDK13 GA發佈：5大特性解讀
- • Java Agent入門實戰（一）-Instrumentation介紹與使用
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
- 1. 升級Gradle後報錯Gradle‘s dependency cache may be corrupt (this sometimes occurs
- 2. Smarter, Not Harder
- 3. mac-2019-react-native 本地環境搭建(xcode-11.1和android studio3.5.2中Genymotion2.12.1 和VirtualBox-5.2.34 )
- 4. 查看文件中關鍵字前後幾行的內容
- 5. XXE萌新進階全攻略
- 6. Installation failed due to: ‘Connection refused: connect‘安卓studio端口占用
- 7. zabbix5.0通過agent監控winserve12
- 8. IT行業UI前景、潛力如何？
- 9. Mac Swig 3.0.12 安裝
- 10. Windows上FreeRDP-WebConnect是一個開源HTML5代理，它提供對使用RDP的任何Windows服務器和工作站的Web訪問
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. A Simple Framework for Contrastive Learning of Visual Representations
- 2. 論文寫作解讀：A Simple Framework for Contrastive Learning of Visual Representations
- 3. Article Analysis(AA): A Simple Framework for Contrastive Learning of Visual Representations
- 4. Deep Temporal Clustering: Fully unsupervised learning of time-domain features
- 5. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations學習與理解
- 6. Exploring the teaching of deep learning in neural networks
- 7. [ICML19] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
- 8. Contrastive Learning Of Structured World Models
- 9. The Rise of Meta Learning
- 10. 《A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling》筆記