  Disentanglement is defined as the problem of learninga representation that
can separate the distinct, informativefactors of variations of data. Learning
such a representa-tion may be critical for developing explainable and
human-controllable Deep Generative Models (DGMs) in artificialintelligence.
However, disentanglement in GANs is not a triv-ial task, as the absence of
sample likelihood and posteriorinference for latent variables seems to prohibit
the forwardstep. Inspired by contrastive learning (CL), this paper, froma new
perspective, proposes contrastive disentanglement ingenerative adversarial
networks (CD-GAN). It aims at dis-entangling the factors of inter-class
variation of visual datathrough contrasting image features, since the same
factorvalues produce images in the same class. More importantly,we probe a
novel way to make use of limited amount ofsupervision to the largest extent, to
promote inter-class dis-entanglement performance. Extensive experimental
resultson many well-known datasets demonstrate the efficacy ofCD-GAN for
disentangling inter-class variation.
