  Recent successes of deep learning-based recognition rely on maintaining the
content related to the main-task label. However, how to explicitly dispel the
noisy signals for better generalization in a controllable manner remains an
open issue. For instance, various factors such as identity-specific attributes,
pose, illumination and expression affect the appearance of face images.
Disentangling the identity-specific factors is potentially beneficial for
facial expression recognition (FER). This chapter systematically summarize the
detrimental factors as task-relevant/irrelevant semantic variations and
unspecified latent variation. In this chapter, these problems are casted as
either a deep metric learning problem or an adversarial minimax game in the
latent space. For the former choice, a generalized adaptive (N+M)-tuplet
clusters loss function together with the identity-aware hard-negative mining
and online positive mining scheme can be used for identity-invariant FER. The
better FER performance can be achieved by combining the deep metric loss and
softmax loss in a unified two fully connected layer branches framework via
joint optimization. For the latter solution, it is possible to equipping an
end-to-end conditional adversarial network with the ability to decompose an
input sample into three complementary parts. The discriminative representation
inherits the desired invariance property guided by prior knowledge of the task,
which is marginal independent to the task-relevant/irrelevant semantic and
latent variations. The framework achieves top performance on a serial of tasks,
including lighting, makeup, disguise-tolerant face recognition and facial
attributes recognition. This chapter systematically summarize the popular and
practical solution for disentanglement to achieve more discriminative visual
recognition.
