Image Cption：Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
三個挑戰：模式崩潰、延遲反饋、策略網絡熱身損耗時間長 這篇paper： 首先通過將段落標題作爲一個長期的決策過程進行建模，並將狀態轉換的預測不確定性作爲內在獎勵進行測量，該模型被激勵去記憶準確但很少被發現的描述單詞，而不是那些頻繁使用的通用模式。這樣使得我們最後生成的段落內容生動有趣； 其次，由於評價的外部獎勵只能在完整段落生成後有效，我們通過考慮連續動作的相關性，用視察學習法估計每一步的期望值。
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相關文章
- 1. Deep Reinforcement Learning for Dialogue Generation
- 2. Image Paragraph論文合輯
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- 5. Mind‘s eye: A recurrent visual representation for image caption generation
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- 7. Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
- 8. 閱讀《SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segme》
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- 10. Automatic Trimap Generation for Image Matting