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knok 2017/07/04

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

View PDF Abstract:We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional la

knok 2017/07/04

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Multi-Scale Context Aggregation by Dilated Convolutions

State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions t

knok 2017/07/04

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knok 2017/07/04

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Dropout as a Bayesian Approximation: Appendix

We show that a neural network with arbitrary depth and non-linearities, with dropout applied before every weight layer, is mathematically equivalent to an approximation to a well known Bayesian model. This interpretation might offer an explanation to some of dropout's key properties, such as its robustness to over-fitting. Our interpretation allows us to reason about uncertainty in deep learning,

knok 2017/07/04

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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator

knok 2017/07/04

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Home Â» ãƒªã‚½ãƒ¼ã‚¹ Â» ç§ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ Â» ã€è¨˜äº‹æ›´æ–°ã€‘ç§ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ã€Œãƒ‡ã‚£ã‚¸ã‚¿ãƒ«ã‚²ãƒ¼ãƒ ã®äººå·¥çŸ¥èƒ½ï¼ˆArtificial Intelligence in Digital Gameï¼‰ã€ ä¸‰å®… é™½ä¸€éƒŽï¼ˆæ ªå¼ä¼šç¤¾ã‚¹ã‚¯ã‚¦ã‚§ã‚¢ãƒ»ã‚¨ãƒ‹ãƒƒã‚¯ã‚¹ï¼‰ ã¯ã˜ã‚ã«ãƒ‡ã‚£ã‚¸ã‚¿ãƒ«ã‚²ãƒ¼ãƒ ã«ãŠã‘ã‚‹äººå·¥çŸ¥èƒ½ç ”ç©¶ã¯ã€ç¾å®Ÿãã£ãã‚Šã®ä»®æƒ³ã‚·ãƒŸãƒ¥ãƒ¬ãƒ¼ã‚·ãƒ§ãƒ³ç©ºé–“ã§ã€ã‚ãƒ£ãƒ©ã‚¯ã‚¿ã¨ã„ã†èº«ä½“ã‚’æŒã¤çŸ¥èƒ½ã‚’ä½œã£ã¦è©¦ã™ã€ã¨ã„ã†å–ã‚Šçµ„ã¿ã§ã™ã€‚ãã“ã§ã¯è¤‡é›‘ãªï¼“æ¬¡å…ƒã®åœ°å½¢ã‚’ä½œã£ã¦ã€è¤‡é›‘ãªèº«ä½“ã®å‹•ä½œã‚’æ±ºã‚ã•ã›ã‚‹ã“ã¨ã‚‚ã§ãã‚Œã°ï¼ˆä¾‹ãˆã°ãƒ•ã‚¡ãƒ³ã‚¿ã‚¸ãƒ¼ä¸–ç•Œã§æ¸“è°·ã‚’æ©ã‹ã›ã‚‹ãªã©ï¼‰ã€é•·æ™‚é–“ã«åŠã¶è¤‡é›‘ãªãƒŸãƒƒã‚·ãƒ§ãƒ³ã‚’è§£ã‹ã›ã‚‹ï¼ˆä¾‹ãˆã°ã€æ•µã®åŸºåœ°ã«æ½œå…¥ã—ã¦è¨è¨ˆæ›¸ã‚’æŒã£ã¦å¸°ã‚‹ï¼‰ã“ã¨ã‚‚ã§ãã¾ã™ã€‚æ™‚é–“ã‚’ç´ æ—©ãé€²ã‚ã¦é€²åŒ–ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚„å¦ç¿’ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ç”¨ã„ã‚‹ã“ã¨ã‚‚ã§ãã€ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã§å‹•ä½œã‚„é¸æŠžã‚’å¦ç¿’ã™ã‚‹ã“ã¨ã‚‚ã§ãã¾ã™ã€‚ãƒ‡ã‚£ã‚¸ã‚¿ãƒ«ã‚²ãƒ¼ãƒ ã¯å¤šæ§˜ã§å·¨å¤§ãªå®Ÿé¨“ã®å ´ã§ã‚‚ã‚ã‚Š

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2017å¹´7æœˆ4æ—¥ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (8ä»¶)

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Multi-Scale Context Aggregation by Dilated Convolutions

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Dropout as a Bayesian Approximation: Appendix

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

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