【論文閱讀】【CVPR2017】Temporal Attention-Gated Model for Robust Sequence Classification
Temporal Attention-Gated Model for Robust SequenceClassification 用於序列分類的典型技術是針對已經分割良好的序列而設計的，編輯刪除嘈雜或不相關的部分。所以，這樣的方法不能輕易應用於實際應用中預期的嘈雜序列。在本文中，我們介紹時間注意門控模型（TAGM）集成了注意模型和門控週期性神經網絡的想法，以更好地處理噪聲或非分段序列。具體而言，我
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相關文章
- 1. 論文閱讀：《Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu》
- 2. Spatio-Temporal Fusion based Convolutional Sequence Learning for Lip Reading論文閱讀
- 3. 論文筆記：Connectionist Temporal Classification: Labelling Unsegmented Sequence
- 4. CTC算法論文閱讀筆記：Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurren
- 5. Enriching Pre-trained Language Model with Entity Information for Relation Classification論文閱讀
- 6. 【論文閱讀筆記】Universal Language Model Fine-tuning for Text Classification
- 7. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 8. Residual Attention Network for Image Classification 論文閱讀
- 9. 論文筆記：SGM: Sequence Generation Model for Multi-label Classification
- 10. 論文解讀：Sequence to Sequence Mixture Model for Diverse Machine Translation