Deep Learning
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, Xiaowei Zhou
Neural Information Processing Systems http://nips.cc/
Learning both Weights and Connections for Efficient Neural Network
Song Han, Jeff Pool, John Tran, William Dally
Neural Information Processing Systems http://nips.cc/
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
Neural Information Processing Systems http://nips.cc/
Stand-Alone Self-Attention in Vision Models
Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jon Shlens
Convolutions are a fundamental building block of modern computer vision systems.
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
In this work, we focus on one-to-many sequence transduction problems, such as extracting multiple sequential sources from a mixture sequence.
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Flow models have recently made great progress at modeling ordinal discrete data such as images and audio.
Certified Adversarial Robustness with Additive Noise
Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
Although a significant body of work on developing defensive models has been considered, most such models are heuristic and are often vulnerable to adaptive attacks.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Emily L. Denton, Soumith Chintala, arthur szlam, Rob Fergus
Neural Information Processing Systems http://nips.cc/
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
Can Qin, Haoxuan You, Lichen Wang, C.-C. Jay Kuo, Yun Fu
However, as far as we are aware, there are few methods yet to achieve domain adaptation directly on 3D point cloud data.