ED-GCAE: Efficient and Adaptive Disentanglement via Shared Features and Dynamic Noise Injection

While Autoencoder-based methods have become a dominant framework in disentangled representation learning, their reliance on simplistic Gaussian density estimation presents significant limitations to better disentanglement performance. The Gaussian Channel Autoencoder GCAE was introduced to address density estimation flexibility yet suffers from high computational costs due to its independent discriminator architecture and sensitivity to noise. To overcome these challenges, we propose ED-GCAE, a novel frame-work designed to improve the efficiency and dynamic adaptability of GCAE. ED-GCAE incorporates a shared feature extraction backbone into the dis-criminator architecture, significantly enhancing computational efficiency and training stability. Concurrently, we introduce a dynamic latent-variable-dependent noise injection mechanism to achieve the balance between disen-tanglement and stability. Experiments demonstrate that ED-GCAE demon-strates superior performance compared to baseline methods, achieving better disentangled representations while exhibiting enhanced training stability and computational efficiency.