논문 리뷰

[논문 리뷰] Are GAN generated images easy to detect? A critical analysis of the state-of-the-art

AI CONNECT에서 진행하는 Fake or Real: AI 생성 이미지 판별 경진대회를 참가하기 위해 여러 가지 논문을 뒤적이던 중에 괜찮은 논문이 있어서 리뷰를 해보려고 한다. https://arxiv.org/abs/2104.02617 Are GAN generated images easy to detect? A critical analysis of the state-of-the-art The advent of deep learning has brought a significant improvement in the quality of generated media. However, with the increased level of photorealism, synthetic media are becomi..

[논문 리뷰] Prioritized Experience Replay (PER)

[1511.05952] Prioritized Experience Replay (arxiv.org) Prioritized Experience Replay Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequ arxiv.org 이 논문은 DQN의 uniformly sampled experience replay..

[논문 리뷰] Dueling Network Architectures for Deep Reinforcement Learning (Dueling DQN)

[1511.06581] Dueling Network Architectures for Deep Reinforcement Learning (arxiv.org) Dueling Network Architectures for Deep Reinforcement Learning In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. In this paper, we pres..

[논문 리뷰] Deep Reinforcement Learning with Double Q-learning (DDQN)

[1509.06461] Deep Reinforcement Learning with Double Q-learning (arxiv.org) Deep Reinforcement Learning with Double Q-learning The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. arxiv.org 이번 논..

[논문 리뷰] Deep Recurrent Q-Learning for Partially Observable MDPs (DRQN)

[1507.06527] Deep Recurrent Q-Learning for Partially Observable MDPs (arxiv.org) Deep Recurrent Q-Learning for Partially Observable MDPs Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article arxi..