Improving EEG-Based Emotion Classification Using Conditional Transfer Learning論文解讀
文章問題提出：情感分類需要每個人提供大量的校準數據但那是費時費力的，怎麼才能不通過增加樣本數據下提高檢測的準確度？ 文章方案：通過條件遷移學習來解決那些樣本分類不好的個體，即從源域中選取與目標域相似的樣本，組成新的源域從而提高檢測的準曲率。 dataset: Oscar soundtrack EEG dataset.26個個體每個人有16段的30-s 30-channel 腦電實驗和自我評價lab
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相關文章
- 1. MPL-GAN:Toward Realistic Meteorological predictive learning Using Conditional GAN論文解讀
- 2. 論文閱讀：《Improving Content-based and Hybrid Music Recommendation using Deep Learning》
- 3. 論文閱讀：Syn2Real Transfer Learning for Image Deraining using Gaussian Processes
- 4. 遷移學習論文閱讀：Transfer Learning via Learning to Transfer
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- 6. 讀論文-DARLA:Improving Zero-Shot Transfer in Reinforcement Learning
- 7. 論文閱讀：A Survey on Transfer Learning
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- 9. 論文解讀：Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
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