English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

State-of-the-art techniques of artificial intelligence, in particular deeplearning, are mostly data-driven. However, collecting and manually labeling alarge scale dataset is both difficult and expensive. A promising alternative isto introduce synthesized training data, so that the dataset size can besignificantly enlarged with little human labor. But, this raises an importantproblem in active vision: given an {\bf infinite} data space, how toeffectively sample a {\bf finite} subset to train a visual classifier? Thispaper presents an approach for learning from synthesized data effectively. Themotivation is straightforward -- increasing the probability of seeing difficulttraining data. We introduce a module named {\bf SampleAhead} to formulate thelearning process into an online communication between a {\em classifier} and a{\em sampler}, and update them iteratively. In each round, we adjust thesampling distribution according to the classification results, and train theclassifier using the data sampled from the updated distribution. Experimentsare performed by introducing synthesized images rendered from ShapeNet modelsto assist PASCAL3D+ classification. Our approach enjoys higher classificationaccuracy, especially in the scenario of a limited number of training samples.This demonstrates its efficiency in exploring the infinite data space.

SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data