We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the …

We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Learning" https://arxiv.org/abs/1312.5602 強化学習にディ…

We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Learning" https://arxiv.org/ab…

The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Lea…

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Learning" https://arx…

One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. Christian Szegedy, et al., "Going Deeper with Co…

To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. Christian Szegedy, et al., "Going Deeper with Convolutions" https://arxiv.org/abs/1409.4842 2014年のILSVRCで1…

This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. Christian Szegedy, et al., "Going Deeper with Convolutions" https://arxiv.org/ab…

The main hallmark of this architecture is the improved utilization of the computing resources inside the network. Christian Szegedy, et al., "Going Deeper with Convolutions" https://arxiv.org/abs/1409.4842 2014年のILSVRCで1位になったディー…