SpineNet is a convolutional neural network backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.

Source: SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

Latest Papers

PAPER DATE
Efficient Scale-Permuted Backbone with Learned Resource Distribution
Xianzhi DuTsung-Yi LinPengchong JinYin CuiMingxing TanQuoc LeXiaodan Song
2020-10-22
Rethinking Pre-training and Self-training
| Barret ZophGolnaz GhiasiTsung-Yi LinYin CuiHanxiao LiuEkin D. CubukQuoc V. Le
2020-06-11
Evolving Normalization-Activation Layers
| Hanxiao LiuAndrew BrockKaren SimonyanQuoc V. Le
2020-04-06
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
| Xianzhi DuTsung-Yi LinPengchong JinGolnaz GhiasiMingxing TanYin CuiQuoc V. LeXiaodan Song
2019-12-10

Tasks

Categories