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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a novel regularization algorithm to train deep neural networks, inwhich data at training time is severely biased. Since a neural networkefficiently learns data distribution, a network is likely to learn the biasinformation to categorize input data. It leads to poor performance at testtime, if the bias is, in fact, irrelevant to the categorization. In this paper,we formulate a regularization loss based on mutual information between featureembedding and bias. Based on the idea of minimizing this mutual information, wepropose an iterative algorithm to unlearn the bias information. We employ anadditional network to predict the bias distribution and train the networkadversarially against the feature embedding network. At the end of learning,the bias prediction network is not able to predict the bias not because it ispoorly trained, but because the feature embedding network successfully unlearnsthe bias information. We also demonstrate quantitative and qualitativeexperimental results which show that our algorithm effectively removes the biasinformation from feature embedding.

Learning Not to Learn: Training Deep Neural Networks with Biased Data