9 citations · 9 across the 7 of their papers we have counts for
9 papers
Efficient Fréchet Distributional Decoder Alignment for Latent Generative Models
Xianghong Fang, Jiajun Zhu, Esma Aimeur +2
Fréchet Distributional (FD) objectives provide an effective distribution-level signal for post-training generative models. For latent generative models, however, existing FD-based…
AudioGAR: Bridging Reconstruction and Generation in Latent Audio Generative Models
Xianghong Fang, Geeyang Tay, Wentao Ma +2
Latent audio generative models are typically trained in two stages: an audio codec is learned first, followed by a latent generative model. This decomposition leads to a decoder tr…
A Latent Distribution Perspective on Evaluating and Improving Latent Generative Models
Xianghong Fang, Wenjie Shu, Tongda Xu +3
In latent generative models, reconstruction quality is often assumed to correlate with generative performance. However, reconstruction FID (rFID) can exhibit weak or even negative…
A Unified Rate-Distortion Perspective on Vector, Product, and Scalar Quantization
Xianghong Fang, Wenlong Mou, Yuan Yuan +2
Discrete visual tokenization, predominantly driven by vector, scalar, and product quantization, lacks a unified conceptual framework for understanding quantization tradeoffs. In th…
VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
Xianghong Fang, Yuan Yuan, Dehan Kong +1
Vector Quantization (VQ) underpins modern discrete visual tokenization. However, training quantization modules for state-of-the-art VQ-based models requires significant computation…
Rethinking The Uniformity Metric in Self-Supervised Learning
Xianghong Fang, Jian Li, Qiang Sun +1
Uniformity plays an important role in evaluating learned representations, providing insights into self-supervised learning. In our quest for effective uniformity metrics, we pinpoi…
Discrete Auto-regressive Variational Attention Models for Text Modeling
Xianghong Fang, Haoli Bai, Jian Li +3
Variational autoencoders (VAEs) have been widely applied for text modeling. In practice, however, they are troubled by two challenges: information underrepresentation and posterior…
Discrete Variational Attention Models for Language Generation
Xianghong Fang, Haoli Bai, Zenglin Xu +2
Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior col…
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
Xianghong Fang, Haoli Bai, Ziyi Guo +3
The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an im…