Transfer Learning for Non-Intrusive Load Monitoring
摘要 - 非侵入式負載監控（NILM）是一種僅從家庭中記錄的主電源恢復源設備的技術。 NILM無法識別，因此是一個挑戰問題，因爲僅給出主電源的設備的推斷功率值不可能是唯一的。爲了緩解不可識別的問題，已經提出了將領域知識結合到NILM中的各種方法，並且在實驗上有效地顯示了這些方法。最近，在這些方法中，深度神經網絡顯示出最佳性能。可以說，最近提出的序列到點（seq2point）學習對於NILM是有希望
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相關文章
- 1. Transfer Learning for Non-Intrusive Load Monitoring
- 2. Multi-Label Transfer Learning for Semantic Similarity
- 3. Discriminative Transfer Learning for General Image Restoration
- 4. transfer learning
- 5. Transfer Learning
- 6. Transfer Learning from Partial Annotations for Whole Brain Segmentation
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- 8. Transfer Learning for Item Recommendations and Knowledge Graph Completion
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- 10. 轉載:A Light Introduction to Transfer Learning for NLP