Latent Zoning Network: A Unified Principle for Generative Modeling,
  Representation Learning, and Classification
Generative modeling, representation learning, and classification are three
core problems in machine learning (ML), yet their state-of-the-art (SoTA)
solutions remain largely disjoint. In this paper, we ask: Can a unified
principle address all three? Such unification could simplify ML pipelines and
foster greater synergy across tasks. We introduce Latent Zoning Network (LZN)
as a step toward this goal. At its core, LZN creates a shared Gaussian latent
space that encodes information across all tasks. Each data type (e.g., images,
text, labels) is equipped with an encoder that maps samples to disjoint latent
zones, and a decoder that maps latents back to data. ML tasks are expressed as
compositions of these encoders and decoders: for example, label-conditional
image generation uses a label encoder and image decoder; image embedding uses
an image encoder; classification uses an image encoder and label decoder. We
demonstrate the promise of LZN in three increasingly complex scenarios: (1) LZN
can enhance existing models (image generation): When combined with the SoTA
Rectified Flow model, LZN improves FID on CIFAR10 from 2.76 to 2.59-without
modifying the training objective. (2) LZN can solve tasks independently
(representation learning): LZN can implement unsupervised representation
learning without auxiliary loss functions, outperforming the seminal MoCo and
SimCLR methods by 9.3% and 0.2%, respectively, on downstream linear
classification on ImageNet. (3) LZN can solve multiple tasks simultaneously
(joint generation and classification): With image and label encoders/decoders,
LZN performs both tasks jointly by design, improving FID and achieving SoTA
classification accuracy on CIFAR10. The code and trained models are available
at https://github.com/microsoft/latent-zoning-networks. The project website is
at https://zinanlin.me/blogs/latent_zoning_networks.html.
68d0d1828adc5cd018d15b5e
Zinan Lin
claimed_verified
2025-09-22T10:31:24.239Z
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Zinan Lin
user
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68d0d1828adc5cd018d15b5f
Enshu Liu
claimed_verified
2025-09-23T02:41:58.772Z
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Enshu Liu
user
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68d0d1828adc5cd018d15b60
Xuefei Ning
claimed_verified
2025-09-23T02:42:00.907Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1678782881444-noauth.jpeg
Xuefei Ning
user
Foxfi
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Junyi Zhu
claimed_verified
2025-09-23T10:07:17.224Z
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Junyi Zhu
user
JJZhu-AI
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Wenyu Wang
68d0d1828adc5cd018d15b63
Sergey Yekhanin
Latent Zoning Network: A Unified Principle for Generative Modeling,
  Representation Learning, and Classification
Generative modeling, representation learning, and classification are three
core problems in machine learning (ML), yet their state-of-the-art (SoTA)
solutions remain largely disjoint. In this paper, we ask: Can a unified
principle address all three? Such unification could simplify ML pipelines and
foster greater synergy across tasks. We introduce Latent Zoning Network (LZN)
as a step toward this goal. At its core, LZN creates a shared Gaussian latent
space that encodes information across all tasks. Each data type (e.g., images,
text, labels) is equipped with an encoder that maps samples to disjoint latent
zones, and a decoder that maps latents back to data. ML tasks are expressed as
compositions of these encoders and decoders: for example, label-conditional
image generation uses a label encoder and image decoder; image embedding uses
an image encoder; classification uses an image encoder and label decoder. We
demonstrate the promise of LZN in three increasingly complex scenarios: (1) LZN
can enhance existing models (image generation): When combined with the SoTA
Rectified Flow model, LZN improves FID on CIFAR10 from 2.76 to 2.59-without
modifying the training objective. (2) LZN can solve tasks independently
(representation learning): LZN can implement unsupervised representation
learning without auxiliary loss functions, outperforming the seminal MoCo and
SimCLR methods by 9.3% and 0.2%, respectively, on downstream linear
classification on ImageNet. (3) LZN can solve multiple tasks simultaneously
(joint generation and classification): With image and label encoders/decoders,
LZN performs both tasks jointly by design, improving FID and achieving SoTA
classification accuracy on CIFAR10. The code and trained models are available
at https://github.com/microsoft/latent-zoning-networks. The project website is
at https://zinanlin.me/blogs/latent_zoning_networks.html.
Latent Zoning Network (LZN) unifies generative modeling, representation learning, and classification by creating a shared latent space for diverse data types.
Latent Zoning Network Gaussian latent space encoders decoders label-conditional image generation image embedding classification Rectified Flow FID MoCo SimCLR unsupervised representation learning joint generation and classification