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深度学习在图像处理中应用总结和优化

(2017-04-08 16:04:25)
标签:

深度学习

图像处理

分类: 深度学习

https://github.com/kwotsin/awesome-deep-vision
from https://github.com/m2dsupsdlclass/lectures-labs
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. LeNet
Simonyan, Karen, and Zisserman. "Very deep convolutional networks for large-scale image recognition." (2014) VGG-16
Simplified version of Krizhevsky, Alex, Sutskever, and Hinton. "Imagenet classification with deep convolutional neural networks." NIPS 2012 AlexNet
He, Kaiming, et al. "Deep residual learning for image recognition." CVPR. 2016. ResNet
Szegedy, et al. "Inception-v4, inception-resnet and the impact of residual connections on learning." (2016)
Canziani, Paszke, and Culurciello. "An Analysis of Deep Neural Network Models for Practical Applications." (May 2016).

classification and localization
Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." CVPR (2016)
Liu, Wei, et al. "SSD: Single shot multibox detector." ECCV 2016
Girshick, Ross, et al. "Fast r-cnn." ICCV 2015
Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." NIPS 2015
Redmon, Joseph, et al. "YOLO9000, Faster, Better, Stronger." 2017

segmentation
Long, Jonathan, et al. "Fully convolutional networks for semantic segmentation." CVPR 2015
Noh, Hyeonwoo, et al. "Learning deconvolution network for semantic segmentation." ICCV 2015
Pinheiro, Pedro O., et al. "Learning to segment object candidates" / "Learning to refine object segments", NIPS 2015 / ECCV 2016
Li, Yi, et al. "Fully Convolutional Instance-aware Semantic Segmentation." Winner of COCO challenge 2016.

弱监督学习 Weak supervision
Joulin, Armand, et al. "Learning visual features from large weakly supervised data." ECCV, 2016
Oquab, Maxime, "Is object localization for free? – Weakly-supervised learning with convolutional neural networks", 2015

Self-supervised learning
Doersch, Carl, Abhinav Gupta, and Alexei A. Efros. "Unsupervised visual representation learning by context prediction." ICCV 2015.


dnn优化
Ren, Mengye, et al. "Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes." 2017
Salimans, Tim, and Diederik P. Kingma. "Weight normalization: A simple reparameterization to accelerate training of deep neural networks." NIPS 2016.
Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. "Layer normalization." 2016.
Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." ICML 2015
Generalization
Understanding deep learning requires rethinking generalization, C. Zhang et al., 2016.
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, N. S. Keskar et al., 2016
1. A strong optimizer is not necessarily a strong learner.
2. DL optimization is non-convex but bad local minima and saddle structures are rarely a problem (on common DL tasks).
3. Neural Networks are over-parametrized but can still generalize.
4. Stochastic Gradient is a strong implicit regularizer.
5. Variance in gradient can help with generalization but can hurt final convergence.
6. We need more theory to guide the design of architectures and optimizers that make learning faster with fewer labels.
7. Overparametrize deep architectures
8. Design architectures to limit conditioning issues:
(1)Use skip / residual connections
(2)Internal normalization layers
(3)Use stochastic optimizers that are robust to bad conditioning
9. Use small minibatches (at least at the beginning of optimization)
10. Use validation set to anneal learning rate and do early stopping
11. Is it very often possible to trade more compute for less overfitting with data augmentation and stochastic regularizers (e.g. dropout).
12. Collecting more labelled data is the best way to avoid overfitting.

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