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cs.CV2025
Towards Scalable Pre-training of Visual Tokenizers for Generation
Jingfeng Yao, Yuda Song, Yucong Zhou +1
The quality of the latent space in visual tokenizers (e.g., VAEs) is crucial for modern generative models. However, the standard reconstruction-based training paradigm produces a l…
cs.CV2022
Learning Low-Rank Representations for Model Compression
Zezhou Zhu, Yucong Zhou, Zhao Zhong
Vector Quantization (VQ) is an appealing model compression method to obtain a tiny model with less accuracy loss. While methods to obtain better codebooks and codes under fixed clu…
cs.CV2021
Learning specialized activation functions with the Piecewise Linear Unit
Yucong Zhou, Zezhou Zhu, Zhao Zhong
The choice of activation functions is crucial for modern deep neural networks. Popular hand-designed activation functions like Rectified Linear Unit(ReLU) and its variants show pro…