Latent Structured Active Learning
| 
105 | 
35 | 
0
Abstract
In this paper we present active learning algorithms in the context of structured prediction problems. To reduce the amount of labeling necessary to learn good models, our algorithms operate with weakly labeled data and we query additional examples based on entropies of local marginals, which are a good surrogate for uncertainty. We demonstrate the effectiveness of our approach in the task of 3D layout prediction from single images, and show that good models are learned when labeling only a handful of random variables. In particular, the same performance as using the full training set can be obtained while only labeling~10% of the random variables.
还没有评论，说两句吧！
热门资源
The Variational S...
Unlike traditional images which do not offer in...
Learning to Predi...
Much of model-based reinforcement learning invo...
Stratified Strate...
In this paper we introduce Stratified Strategy ...
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Rating-Boosted La...
The performance of a recommendation system reli...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
