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Javaã®æ¤œæŸ»ä¾‹å¤–ã¯ã€å‘¼ã³å‡ºã—å´ã§ã€Œã©ã‚“ãªã«æ³¨æ„ã—ã¦ã‚‚防ã’ãªã„ã€ç•°å¸¸ç³» - Qiita
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検査例外å†è€ƒ - Qiita
検査例外ã¯Javaã§å°Žå…¥ã•ã‚ŒãŸä¾‹å¤–ã«å¯¾ã™ã‚‹èˆˆå‘³æ·±ã„機構ã§ã™ã€‚Javaã§å°Žå…¥ã•ã‚ŒãŸå½“åˆã€æ¤œæŸ»ä¾‹å¤–ã¯å¤šãã®äººã€…ã«ç´ 晴らã—ã„解決ç–ã ã¨æ€ã‚ã‚Œã¾ã—ãŸã€‚ã—ã‹ã—ã€æœ€è¿‘ã®ãƒ¡ã‚¸ãƒ£ãƒ¼ãªJVM言語ã§æ¤œæŸ»ä¾‹å¤–をサãƒãƒ¼ãƒˆã™ã‚‹è¨€èªžã¯ã‚ã‚Šã¾ã›ã‚“。JVM言語ã§ãªãã¨ã‚‚ã€ãƒ¡ã‚¸ãƒ£ãƒ¼è¨€èªžã§æ¤œæŸ»ä¾‹å¤–をサãƒãƒ¼ãƒˆã™ã‚‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è¨€èªžã¯2014å¹´ç¾åœ¨1ã¤ã¨ã—ã¦å˜åœ¨ã—ã¾ã›ã‚“*1。 何故検査例外ã¯æ™®åŠã—ãªã‹ã£ãŸã®ã§ã—ょã†ã‹ã€‚çµè«–ã‹ã‚‰è¨€ãˆã°ã€Javaã®æ¤œæŸ»ä¾‹å¤–を用ã„ãŸä¾‹å¤–è¨è¨ˆãŒã‚ã¾ã‚Šã«ã‚‚難ã—ãã€ã»ã¨ã‚“ã©ã®çµ„ç¹”ã«ã¨ã£ã¦è¨è¨ˆã‚³ã‚¹ãƒˆãŒãƒšã‚¤å‡ºæ¥ãªã‹ã£ãŸãŸã‚ã ã¨æ€ã‚ã‚Œã¾ã™ã€‚Javaã®æ¤œæŸ»ä¾‹å¤–ã¯ä¾‹å¤–ã¨ã„ã†ãƒ¬ã‚¤ãƒ¤ãƒ¼ã§å„々ã®å‡¦ç†ã‚’çµã³ã¾ã™ã€‚ã“ã®æ™‚ã€åˆ¥ã€…ã®æŠ½è±¡åº¦ã‚’ã‚ã‚‹å˜ä¸€ã®ä¾‹å¤–ã§çµã‚“ã§ã—ã¾ã†ã¨ã€å•é¡ŒãŒç™ºç”Ÿã—ã¾ã™ã€‚ã¤ã¾ã‚Šã€æŠ½è±¡åº¦ã®ç•°ãªã‚‹å‡¦ç†åŒå£«ã‚’çµã³ã¤ã‘ã‚‹ãŸã³ã«ã€ãã®å·®ç•°ã‚’å¸åŽã™ã‚‹ç‹¬è‡ªä¾‹å¤–表ç¾ãŒå¿…è¦ã«ãªã‚Šã¾ã™ã€‚ã“ã®è¨è¨ˆåˆ¤æ–ã¯ä¸å¯èƒ½ã§ã¯ãªã„ã‚‚ã®ã®ã€ã‹ãªã‚Šé›£ã—
GitHub - david-gpu/srez: Image super-resolution through deep learning
Image super-resolution through deep learning. This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net. Here's an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one i
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Heteroscedastic Relevance Vector Machine
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