Cpgan content-parsing generative
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This document summarizes the CPGAN model for text-to-image synthesis. CPGAN uses a coarse-to-fine generative framework with a memory-attended text encoder to parse text and images into content. It also employs a fine-grained conditional discriminator to match the relationships between words and sub-regions of images. Experimental results show CPGAN outperforms other models on quantitative metrics while using a lighter neural network. However, the quality of generated images still has room for improvement.
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This document summarizes the Plug and Play Language Models (PPLM) approach for controlled text generation. PPLM allows controlling the attributes of text generated by large pretrained language models. It introduces a latent update method using a discriminator to steer generation toward a target attribute while maintaining fluency. The document outlines the PPLM methodology, discusses hyperparameters like alpha values and bag-of-words approaches, and provides examples of attribute-controlled text generation using the PPLM framework.
TRPO(trust region policy optimization)
TRPO (Trust Region Policy Optimization) is a policy gradient method that guarantees monotonic policy improvement while allowing moderate policy updates, by constraining the KL divergence between the old and new policies. It improves on prior methods by using a KL penalty instead of a penalty, and estimates policy updates using samples rather than computing exact updates. TRPO forms the basis for the more practical PPO algorithm.
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plug and play language models a simple approach to controlled text generation
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Cpgan content-parsing generative
- 1. Kyonggi Univ. AI Lab. CPGAN : CONTENT-PARSING GENERATIVE ADVERSARIAL NETWORKS FOR TEXT-TO-IMAGE SYNTHESIS 2021.1.18 정규열 Artificial Intelligence Lab Kyonggi Univiersity
- 2. Kyonggi Univ. AI Lab. Index 도입 배경 CP-GAN Coarse-to-fine Generative Framework Memory-Attended Text Encoder Fine-grained Conditional Discriminator 실험 결론
- 3. Kyonggi Univ. AI Lab. 도입 배경
- 4. Kyonggi Univ. AI Lab. 도입 배경 기존까지 제안된 text-to-image 모델들의 특징 Text을 이미지로 변환하기 위한 구조적 제안이 대부분 이었다. 이 방법은 서로 교차 해석을 해야 하기 때문에 상당히 어렵다. CP GAN Text와 합성된 Image 모두 Parsing한 content 에 집중한다. Memory structure 사용 conditional discriminator를 단어와 이미지의 sub-regions 사이의 관계를 세분화 하도록 맞춤 설정 함 소스코드 : https://github.com/dongdongdong666/CPGAN 학습기능은 미포함(사실상 공개 안 할 것으로 보임)
- 5. Kyonggi Univ. AI Lab. 도입 배경 전체 구조 • 1 : 단어와 다양한 visual 맥락 사이의 일치 시킴 • 2 : 이미지를 의미의 관점에 맞춰 생성함 • 3 : 문장과 생성된 이미지 사이의 일관성을 체크한다.
- 6. Kyonggi Univ. AI Lab. 도입 배경 현재 시점에서 Inception score가 높은 알고리즘 이다.
- 7. Kyonggi Univ. AI Lab. CP-GAN
- 8. Kyonggi Univ. AI Lab. CP-GAN CP-GAN : Coarse-to-fine Generative Framework CP-GAN Attn-GAN 1, 잔차(residual)를 적용함 -> Generator사이의 정보 전달을 용이하게 함. 2, discriminator를 세분화 시킴 -> unconditional, conditional Attn-GAN에서 추가된 요소
- 9. Kyonggi Univ. AI Lab. CP-GAN CP-GAN : Coarse-to-fine Generative Framework Generator Discriminator notations 𝐼 : Generator로 부터 생성된 이미지 X : textual description Encoding 기법이 기존의 Attn_GAN이랑 다르다.
- 10. Kyonggi Univ. AI Lab. CP-GAN CP-GAN : Memory-Attended Text Encoder 기존의 Encoding 방식 현재 학습중인 이미지와 문장에만 집중이 가능하다. 제안하는 방법 과거의 이미지와 문장도 고려한다.
- 11. Kyonggi Univ. AI Lab. CP-GAN CP-GAN : Memory-Attended Text Encoder Memory Construction 단어를 visual 맥락과 서로 맞춘다. (parsing) Visual feature : m : Attention score가 가장 높은 Visual feature를 뽑은 후 가공함
- 12. Kyonggi Univ. AI Lab. CP-GAN CP-GAN : Memory-Attended Text Encoder Text Encoding with Memory 이전에 생성한 m으로부터 Text를 encoding 함. 단어의 embedding 값도 같이 적용한다.(e)
- 13. Kyonggi Univ. AI Lab. CP-GAN CP-GAN : Fine-grained Conditional Discriminator 입력된 자연어와 합성된 이미지를 의미적으로 일치 시킴.
- 14. Kyonggi Univ. AI Lab. 실험
- 15. Kyonggi Univ. AI Lab. 실험 정량적 평가 여러가지 평가지표 모두 CP GAN이 우수하다.
- 16. Kyonggi Univ. AI Lab. 실험 정량적 평가 비교적 가벼운 신경망으로도 성능이 좋았다.
- 17. Kyonggi Univ. AI Lab. 실험 정성적 평가
- 18. Kyonggi Univ. AI Lab. 실험 정성적 평가
- 19. Kyonggi Univ. AI Lab. 실험 직접 실행한 결과 Sever airplanes are parked on an airport runway. The room is situated on the dark side of the house.
- 20. Kyonggi Univ. AI Lab. 결론
- 21. Kyonggi Univ. AI Lab. 결론 Text와 Image를 Parsing 하여 의미적으로 매칭 시키려 하였다. Attn Gan에서 Text와 Image encoder 부분을 수정 하였다. 단어와 sub region간의 연관성을 높이려 하였다. fine-grained conditional discriminator 개인적의견 이전 모델에 비해 성능은 많이 향상되었다. 또한 이전 모델에 비해 상대적으로 가벼운 편이다. 그러나 생성된 품질은 아직은 아쉽다.