• [Pytorch] Alphachu (Ape-X DQN)

    Alphachu: Ape-x DQN implementation of Pikachu Volleyball [Demo] [Paper] Training agents to learn how to play Pikachu Volleyball. Architecture is based on Ape-x DQN from the paper. The game is in exe file which makes the whole problem much more complicated than other Atari games. I built python environment to...


  • [Pytorch] VAE-NF

    Pytorch implementation of Variational Inference with Normalizing Flows. https://github.com/Lyusungwon/generative_models_pytorch Reference https://github.com/ex4sperans/variational-inference-with-normalizing-flows


  • Spectral Normalization for Generative Adversarial Networks

    WHY? The largest drawback of training Generative Adversarial Network (GAN) is its instability. Especially, the power of discriminator greatly affect the performance of GAN. This paper suggests to weaken the discriminator by restricting the functional space of it to stablize the training. Note Matrix norm can be defined in various...


  • A Two-Step Disentanglement Method

    WHY? This paper wanted to disentangle the label related and label unrelated information from data. The model of this paper is simpler and more effective than that of Disentangling Factors of Variation in Deep Representations Using Adversarial Training. WHAT? Let S represents labeled information and Z represents the rest. The...


  • Deep Contextualized Word Representations

    WHY? Former word representations such as Word2Vec or GloVe didn’t contained linguistic context. WHAT? This paper suggests embedding from language model(ELMO) to include context information of word. Assume that x is context independent representation (token embedding or CNN over characters). Bi-directional l layers LSTM are used to predict previous or...