| Deep Learning Starting Points
As is the case with most research areas, the most influential papers come from a wide variety of sources. There is no single definitive source for the top research.
This short article is intended for students who have a computer science background and are becoming interested in the subject of deep learning.
Some recommended publications are
CVPR - Computer Vision and Pattern Recognition Conference
ICCV - International Conference on Computer Vision
NIPS - Neural Information Processing Systems
MM - ACM International Conference on Multimedia
MMM - (ACM/IEEE) Multimedia Modeling Conference
Neural Networks - Journal on Neural Networks
Neurocomputing - Journal on Neural Computation
IEEE Trans MM - IEEE Transactions on Multimedia
IJCV - International Journal of Computer Vision
Also, many research reports are posted to arxiv.org, however, keep in mind that arxiv.org does not do peer review.
Many papers are easily accessible using Google Scholar (scholar.google.com) - just paste in the title of the article and you will usually immediately find it. Note that if you are at a Leiden University or LIACS IP address (e.g. LIACS wireless), then you should have the option to download all papers from ACM (www.acm.org/dl), IEEE Computer Society (www.computer.org), Springer (www.springer.com), and Elsevier (www.elsevier.com) for free.
If the paper is not downloadable from Google Scholar or any of the above, it is also standard practice to contact the first author of the paper and request a copy. They will usually be happy to send you a copy.
I suggest getting started by reading up on the surveys such as
"Deep learning in neural networks: An overview", in Neural Networks, 2015
"Deep learning for visual understanding: A review", in Neurocomputing, 2016
and interesting benchmarks such as
"Imagenet large scale visual recognition challenge", IJCV, 2015
and also neural programming frameworks such as Caffe:
Convolutional architecture for fast feature embedding", ACM MM, 2014
TensorFlow: A System for Large-Scale Machine Learning", OSDI, 2016
and the top deep learning approaches from the ImageNet competition such as AlexNet:
"ImageNet Classiﬁcation with Deep Convolutional Neural Networks", NIPS, 2012.
"Very deep convolutional networks for large-scale image recognition", Arxiv, 2014
and Residual Networks:
"Deep residual learning for image recognition", CVPR, 2016
Other interesting deep networks are
"Densely Connected Convolutional Networks", CVPR, 2017
and Convolutional Fusion Networks (CFN)
"On the Exploration of Convolutional Fusion Networks for Visual Recognition", MMM, 2017
In most cases, the authors provide the actual network for download also.
Please be aware that the learning curve is rather steep for deep learning. The training process can be quite tricky and it may be unclear or incomplete from the research paper. There are numerous things the authors may do to maximize the accuracy which may have nothing to do with the actual CNN. Your mileage may vary...
Dr. Michael S. Lew