Flowing Backwards: Improving Normalizing Flows via Reverse Representation Alignment
Normalizing Flows (NFs) are a class of generative models distinguished by a mathematically invertible architecture, where the forward pass transforms data into a latent space for density estimation, and the reverse pass generates new samples from this space. This characteristic creates an intrinsic synergy between representation learning and data generation. However, the generative quality of standard NFs is limited by poor semantic representations from log-likelihood optimization. To remedy this, we propose a novel alignment strategy that creatively leverages the invertibility of NFs: instead of regularizing the forward pass, we align the intermediate features of the generative (reverse) pass with representations from a powerful vision foundation model, demonstrating superior effectiveness over naive alignment. We also introduce a novel training-free, test-time optimization algorithm for classification, which provides a more intrinsic evaluation of the NF's embedded semantic knowledge. Comprehensive experiments demonstrate that our approach accelerates the training of NFs by over 3.3times, while simultaneously delivering significant improvements in both generative quality and classification accuracy. New state-of-the-art results for NFs are established on ImageNet 64times64 and 256times256. Our code is available at https://github.com/MCG-NJU/FlowBack.
692cfbae4397b1ec214f675a
Yang Chen
claimed_verified
2025-12-01T09:11:25.208Z
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Chen Yang
Y-Sisyphus
user
Y-Sisyphus
692cfbae4397b1ec214f675b
Xiaowei Xu
692cfbae4397b1ec214f675c
Shuai Wang
claimed_verified
2025-12-04T10:34:46.581Z
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wangshuai
wangsssssss
user
wangsssssss
692cfbae4397b1ec214f675d
Chenhui Zhu
692cfbae4397b1ec214f675e
Ruxue Wen
692cfbae4397b1ec214f675f
Xubin Li
692cfbae4397b1ec214f6760
Tiezheng Ge
692cfbae4397b1ec214f6761
Limin Wang
Flowing Backwards: Improving Normalizing Flows via Reverse Representation Alignment
Normalizing Flows (NFs) are a class of generative models distinguished by a mathematically invertible architecture, where the forward pass transforms data into a latent space for density estimation, and the reverse pass generates new samples from this space. This characteristic creates an intrinsic synergy between representation learning and data generation. However, the generative quality of standard NFs is limited by poor semantic representations from log-likelihood optimization. To remedy this, we propose a novel alignment strategy that creatively leverages the invertibility of NFs: instead of regularizing the forward pass, we align the intermediate features of the generative (reverse) pass with representations from a powerful vision foundation model, demonstrating superior effectiveness over naive alignment. We also introduce a novel training-free, test-time optimization algorithm for classification, which provides a more intrinsic evaluation of the NF's embedded semantic knowledge. Comprehensive experiments demonstrate that our approach accelerates the training of NFs by over 3.3times, while simultaneously delivering significant improvements in both generative quality and classification accuracy. New state-of-the-art results for NFs are established on ImageNet 64times64 and 256times256. Our code is available at https://github.com/MCG-NJU/FlowBack.
A novel alignment strategy and test-time optimization algorithm enhance the generative quality and classification accuracy of Normalizing Flows by leveraging invertibility and vision foundation models.
Normalizing Flows generative models invertible architecture latent space density estimation log-likelihood optimization semantic representations alignment strategy vision foundation model classification ImageNet