2020-02-07 18:47:12
Source：https://medium.com/@dobko_m/neurips-2019-computer-vision-recap-ddd26b13337c
Recap of some papers from 33rd Conference on Neural Information Processing Systems
Papers
This is an overview (notes) of NeurIPS 2019 that was held during 9–14 of December in Vancouver. Over 13,000 participants. 2 days of workshops, a day of tutorials and 3 days of main conference.
In this post I shortly describe some of the papers which were presented and caught my attention. All the works mentioned in this post are in Computer Vision domain, which is my field of research.
Full-Gradient Representation for Neural Network Visualization
Suraj Srinivas, François Fleuret
FullGrad saliency which incorporates both input-gradients and feature-level bias-gradients, thus, satisfies two notions of importance: local (model sensitivity to input) and global (completeness of saliency map).
Emergence of Object Segmentation in Perturbed Generative Models
Adam Bielski, Paolo Favaro
Paper
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen
GPipes provides a way to increase quality even for datasets with smaller sizes using transfer learning or multitask learning. The experiments showed that deeper networks transfer better while wider models memorize better.
Learning Conditional Deformable Templates with Convolutional Networks
Adrian Dalca, Marianne Rakic, John Guttag, Mert Sabuncu
Code.
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
Code.
Saccader: Improving Accuracy of Hard Attention Models for Vision
Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le
Code.
Unsupervised Object Segmentation by Redrawing
Mickaël Chen, Thierry Artières, Ludovic Denoyer
Paper.
Learning the full model for object segmentation. Learned neural networks are represented in bold colored lines
Approximate Feature Collisions in Neural Nets
Ke Li, Tianhao Zhang, Jitendra Malik
Paper.
Grid Saliency for Context Explanations of Semantic Segmentation
Lukas Hoyer, Mauricio Munoz, Prateek Katiyar, Anna Khoreva, Volker Fischer
Paper.
Fooling Neural Networks Interpretations via Adversarial Model Manipulation
Juyeon Heo , Sunghwan Joo , Taesup Moon
Paper.
A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
VarGrad.
HYPE -Human eYe Perceptual Evaluation: A benchmark for generative models
Sharon Zhou, Mitchell L. Gordon et al.
efficient in cost and time.
Region Mutual Information Loss for Semantic Segmentation
Shuai Zhao, Yang Wang , Zheng Yang, Deng Cai
Code.
Multi-source Domain Adaptation for Semantic Segmentation
Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
Paper.
Thank you for reading!