This is a curated list of the most cited deep learning papers (since 2012) posted by Terry Taewoong Um.
Source for picture: What is deep learning and how does it work?
The repository is broken down into the following categories:
- Understanding / Generalization / Transfer
- Optimization / Training Techniques
- Unsupervised / Generative Models
- Convolutional Network Models
- Image Segmentation / Object Detection
- Image / Video / Etc
- Recurrent Neural Network Models
- Natural Language Process
- Speech / Other Domain
- Reinforcement Learning / Robotics
- More Papers from 2016
For instance, the first category contains the following articles:
- Distilling the knowledge in a neural network (2015), G. Hinton et al. [pdf]
- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. [pdf]
- How transferable are features in deep neural networks? (2014), J. Yosinski et al. [pdf]
- CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. [pdf]
- Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]
- Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf]
- Decaf: A deep convolutional activation feature for generic visual recognition (2014), J. Donahue et al. [pdf]
Read the full list here.
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