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.
Improving Explainability in Deep Learning: A Novel Approach to Independence-Constrained Disentangled Representation Learning
This paper argues that incorporating both mutual information and independence constraints within a generative adversarial network (GAN) framework can significantly improve the quality of disentangled representations in deep learning, leading to enhanced explainability and controllability.
認識論的観点から見た独立性制約付き disentangled 表現学習
深層学習の解釈可能性を高めるために、潜在変数間の独立性に着目した新しい表現学習手法を提案し、その有効性を検証する。
인식론적 관점에서 본 독립성 제약 기반의 disentangled representation learning
인간의 인지 과정을 모방한 two-level latent space framework를 통해 latent variable 간의 독립성 및 인과 관계를 명확히 정의하고, 이를 기반으로 disentangled representation learning의 성능을 향상시키는 방법을 제시한다.
Unsupervised Learning of Disentangled Representations by Analyzing Sparse Transformations in Sequential Data
This paper introduces Sparse Transformation Analysis (STA), a novel unsupervised learning framework that disentangles transformations from sequential data by factorizing latent variable transformations into sparse components, achieving state-of-the-art performance in unsupervised approximate equivariance and data likelihood.
Disentangled Representations Emerge Naturally in Multi-Task Learning Agents Operating in Noisy Environments
Optimally solving multiple tasks concurrently within a noisy environment compels agents, both biological and artificial, to learn disentangled representations of the underlying data, leading to superior generalization capabilities.
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