Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Loss Function Entropy Regularization for Diverse Decision Boundaries   Is it possible to train several classifiers to perform meaningful
crowd-sourcing to produce a better prediction label set without any
ground-truth annotation? In this paper, we will attempt to modify the
contrastive learning objectives to automatically train a self-complementing
ensemble to produce a state-of-the-art prediction on the CIFAR10 and
CIFAR100-20 task. This paper will present a remarkably simple method to modify
a single unsupervised classification pipeline to automatically generate an
ensemble of neural networks with varied decision boundaries to learn a larger
feature set of classes. Loss Function Entropy Regularization (LFER), are
regularization terms to be added upon the pre-training and contrastive learning
objective functions, gives us a gear to modify the entropy state of the output
space of unsupervised learning, thereby diversifying the latent representation
of decision boundaries of neural networks. Ensemble trained with LFER have
higher successful prediction accuracy for samples near decision boundaries.
LFER is a effective gear to perturb decision boundaries, and has proven to be
able to produce classifiers that beat state-of-the-art at contrastive learning
stage. Experiments show that LFER can produce an ensemble where each have
accuracy comparable to the state-of-the-art, yet have each have varied latent
decision boundaries. It allows us to essence perform meaningful verification
for samples near decision boundaries, encouraging correct classification of
near-boundary samples. By compounding the probability of correct prediction of
a single sample amongst an ensemble of neural network trained, our method is
able to improve upon a single classifier by denoising and affirming correct
feature mappings.

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The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: math-ph