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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presence of noise in the labels of large scale facial expression datasets hasbeen a key challenge towards Facial Expression Recognition (FER) in the wild.During early learning stage, deep networks fit on clean data. Then, eventually,they start overfitting on noisy labels due to their memorization ability, whichlimits FER performance. This work proposes an effective training strategy inthe presence of noisy labels, called as Consensual Collaborative Training (CCT)framework. CCT co-trains three networks jointly using a convex combination ofsupervision loss and consistency loss, without making any assumption about thenoise distribution. A dynamic transition mechanism is used to move fromsupervision loss in early learning to consistency loss for consensus ofpredictions among networks in the later stage. Inference is done using a singlenetwork based on a simple knowledge distillation scheme. Effectiveness of theproposed framework is demonstrated on synthetic as well as real noisy FERdatasets. In addition, a large test subset of around 5K images is annotatedfrom the FEC dataset using crowd wisdom of 16 different annotators and reliablelabels are inferred. CCT is also validated on it. State-of-the-art performanceis reported on the benchmark FER datasets RAFDB (90.84%) FERPlus (89.99%) andAffectNet (66%). Our codes are available at https://github.com/1980x/CCT.

Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations