cGAN(conditional generative model) what is cGAN? Translating an image given "condition" P(X|condition) condition을 따르는 확률분포를 학습하는 것 Generative model vs Conditional generative model - Generative model generates a random sample - Conditional generative model generates a random smaple under the given "condition" Example of conditional generative model P(high resolution audio|low resolution audio), P( English sentence|.. 2023. 12. 7. Generative Model Basic 2 Maximum Likelihood Learning - parameter가 주어졌을 때 데이터가 얼마나 likely 한지(뭐랑?), 이를 높이는 방향으로 학습함 - 뭘 Metric으로 삼을까? -> KL-divergence - minimizing KL-divergence Maximizing the expected log-likelihood - empirical log-likelihood is approximation of expected log-likelihood? - ERM(empirical risk minimization)을 보통 사용하지만 overfitting 문제를 갖고 있음 *jensen-shannon divegence(GAN), Wasserstein distance(WAE, AAE)등이 metr.. 2023. 12. 5. Generative model Basic 1 P(x)를 학습한다 : input x에 대한 확률분포를 학습한다 Suppose that we are given images of dogs we want to learn a probability distribution p(x) such that - Generation : If we sample x~ p(x),x~ should look like a dog - Density estimation : p(x) shoud be high if x looks like a dog, and low otherwise. - This is also known as explicit models. - Then, how can we represent p(x)? 기본.. 2023. 12. 5. Conditional Generative model Translating an image given "condition" We can explicitly generate an image corresponding to a given "condition" Generative model vs Conditional generative model - Generative model generates a random sample - conditional generative model generates a random sample under the give "condition" 2023. 12. 5. 이전 1 다음