DrFER: Disentangled Representations for 3D Facial Expression Recognition
The author introduces DrFER, a method for disentangling expression features from identity information in 3D facial expression recognition. By employing a dual-branch framework and innovative loss functions, DrFER achieves superior performance in recognizing facial expressions.
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning
A-O disentanglement framework for CZSL using Class-specified Cascaded Network.
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
DrFER introduces disentangled representation learning to enhance 3D facial expression recognition by effectively separating expression and identity information.
Tripod: A Novel Approach to Disentangled Representation Learning Using Three Complementary Inductive Biases
Combining three complementary inductive biases - data compression into a grid-like latent space, collective independence amongst latents, and minimal functional influence of any latent on how other latents determine data generation - can significantly improve disentangled representation learning performance compared to using any single bias alone.
Disentangled Representation Learning: Extracting Meaningful and Independent Factors from Observed Data
Disentangled Representation Learning (DRL) aims to learn representations that can identify and disentangle the underlying factors of variation in observed data, leading to explainable, controllable and generalizable models.
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