Research map / Image generation / Autoregressive and latent generation
Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
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Proposes Latent Zoning Network (LZN), which maps each data type's samples to disjoint zones of a shared Gaussian latent space via flow-matching-based latent computation and soft latent alignment, and evaluates it on image generation (with Rectified Flow), unsupervised ImageNet representation learning, and joint CIFAR10 generation and classification.
Problem
Generative modeling, representation learning, and classification use disjoint state-of-the-art methods; the paper asks whether a single latent-space principle can unify all three.
Approach
Build a shared Gaussian latent space where each data type has an encoder mapping samples to disjoint latent zones (computed by integrating flow matching from encoder anchor points), plus a soft alignment loss that links zones across data types; tasks become encoder-decoder compositions.
Key results
- On CIFAR10 unconditional generation, RF+LZN improves FID from 2.76 to 2.59 and reduces reconstruction error from 0.83 to 0.41.
- Unsupervised ImageNet linear probing with ResNet-50 outperforms MoCo by 9.3% and SimCLR by 0.2% (per the abstract's claim).
- Joint generation and classification on CIFAR10 improves FID and reaches classification accuracy near SoTA, reportedly lagging the best CIFAR10 result by about 1%.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · CIFAR10 | FID: 2.76 -> 2.59 | Helped |
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · AFHQ-Cat | FID: 6.08 -> 5.68 | Helped |
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · CelebA-HQ | FID: 6.95 -> 7.17 | Hurt |
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · LSUN-Bedroom | FID: 6.25 -> 5.95 | Helped |
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · CIFAR10 | Reconstruction error: 0.83 -> 0.41 | Helped |
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · AFHQ-Cat | Reconstruction error: 17.92 -> 10.29 | Helped |
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · CelebA-HQ | Reconstruction error: 26.20 -> 15.90 | Helped |
| Add LZN latents as an extra conditioning input to RF | Rectified Flow · LSUN-Bedroom | Reconstruction error: 48.72 -> 37.01 | Helped |
| Add LZN label and image encoders/decoders for joint generation and classification | Rectified Flow · CIFAR10 conditional generation | FID: 2.47 -> 2.40 | Helped |
| Use LZN for unsupervised representation learning instead of MoCo | ResNet-50 linear classifier on ImageNet · ImageNet validation linear classification | Top-1 accuracy: +9.3% | Helped |
| Use LZN for unsupervised representation learning instead of SimCLR | ResNet-50 linear classifier on ImageNet · ImageNet validation linear classification | Top-1 accuracy: +0.2% | Helped |
| Replace batch normalization with group normalization in the ResNet-50 encoder (ablation) | LZN representation learning · ImageNet | Top-1 accuracy | No clear effect |
Surprising findings
- Representation learning is achieved without any auxiliary contrastive or classification loss, using only latent alignment.
Limitations
- Training requires backpropagating through the flow-matching trajectory, which is computationally expensive.
- Representation and classification results lag SoTA (e.g., DINO, ViT-based methods) and use only ResNet-50 without architecture optimization.
- Minibatch approximation breaks the disjoint-zone property across batches; experiments focus only on images and at most two tasks.
Experimental setup
Datasets: CIFAR10 AFHQ-Cat CelebA-HQ LSUN-Bedroom ImageNet
Benchmarks: CIFAR10 AFHQ-Cat CelebA-HQ LSUN-Bedroom ImageNet
Metrics: FID sFID IS precision recall CMMD reconstruction error top-1 linear classification accuracy classification accuracy
Baselines: Rectified Flow MoCo SimCLR RCG Diffusion Autoencoder DINO
Builds on: Rectified Flow flow matching SimCLR MoCo consistency models
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