  Multilabel representation learning is recognized as a challenging problem
that can be associated with either label dependencies between object categories
or data-related issues such as the inherent imbalance of positive/negative
samples. Recent advances address these challenges from model- and data-centric
viewpoints. In model-centric, the label correlation is obtained by an external
model designs (e.g., graph CNN) to incorporate an inductive bias for training.
However, they fail to design an end-to-end training framework, leading to high
computational complexity. On the contrary, in data-centric, the realistic
nature of the dataset is considered for improving the classification while
ignoring the label dependencies. In this paper, we propose a new end-to-end
training framework -- dubbed KMCL (Kernel-based Mutlilabel Contrastive
Learning) -- to address the shortcomings of both model- and data-centric
designs. The KMCL first transforms the embedded features into a mixture of
exponential kernels in Gaussian RKHS. It is then followed by encoding an
objective loss that is comprised of (a) reconstruction loss to reconstruct
kernel representation, (b) asymmetric classification loss to address the
inherent imbalance problem, and (c) contrastive loss to capture label
correlation. The KMCL models the uncertainty of the feature encoder while
maintaining a low computational footprint. Extensive experiments are conducted
on image classification tasks to showcase the consistent improvements of KMCL
over the SOTA methods. PyTorch implementation is provided in
\url{https://github.com/mahdihosseini/KMCL}.
