Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consensual Collaborative Training And Knowledge Distillation Based
  Facial Expression Recognition Under Noisy Annotations   Presence of noise in the labels of large scale facial expression datasets has
been 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, which
limits FER performance. This work proposes an effective training strategy in
the presence of noisy labels, called as Consensual Collaborative Training (CCT)
framework. CCT co-trains three networks jointly using a convex combination of
supervision loss and consistency loss, without making any assumption about the
noise distribution. A dynamic transition mechanism is used to move from
supervision loss in early learning to consistency loss for consensus of
predictions among networks in the later stage. Inference is done using a single
network based on a simple knowledge distillation scheme. Effectiveness of the
proposed framework is demonstrated on synthetic as well as real noisy FER
datasets. In addition, a large test subset of around 5K images is annotated
from the FEC dataset using crowd wisdom of 16 different annotators and reliable
labels are inferred. CCT is also validated on it. State-of-the-art performance
is reported on the benchmark FER datasets RAFDB (90.84%) FERPlus (89.99%) and
AffectNet (66%). Our codes are available at https://github.com/1980x/CCT.

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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph