728x90 AI23 Deep Residual Learning for Image Recognition(ResNet)(2015) Reivew Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with arxiv.org 0. 핵심 요약 이전보다 더 network를 깊게 사용할 수 있도록 해주는 residual learning framework 제안 152 layers로 I.. 2024. 1. 7. Very Deep Convolutional Networks For Large-Scale Image Recognition(VGG)(2014) Review Very Deep Convolutional Networks for Large-Scale Image Recognition In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x arxiv.org 0. 핵심요약 깊이를 늘려 의미 있는 성능 향상을 이뤄냈다 3 x 3 filter를 통해 다른 filter들을 대체.. 2024. 1. 3. ImageNet Classification with Deep Convolutional Neural Networks(AlexNet)(2012) Review ImageNet Classification with Deep Convolutional Neural Networks Requests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with their co-authors prior to reque papers.nips.cc 0. 핵심 요약 - AlexNet이라는 새로운 모델을 제시하였으며, CNN의 가능성을 보여준 논문 - 현재에는 .. 2024. 1. 1. 이전 1 ··· 3 4 5 6 다음 728x90
