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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2602.18904 |
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| _version_ | 1866910029101137920 |
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| author | Lu, Hao Koyun, Onur C. Guo, Yongxin Zhu, Zhengjie Alili, Abbas Gurcan, Metin Nafi |
| author_facet | Lu, Hao Koyun, Onur C. Guo, Yongxin Zhu, Zhengjie Alili, Abbas Gurcan, Metin Nafi |
| contents | Vector-quantized autoencoders deliver high-fidelity latents but suffer inherent flaws: the quantizer is non-differentiable, requires straight-through hacks, and is prone to collapse. We address these issues at the root by replacing VQ with a simple, principled, and fully differentiable alternative: an online PCA bottleneck trained via Oja's rule. The resulting model, PCA-VAE, learns an orthogonal, variance-ordered latent basis without codebooks, commitment losses, or lookup noise. Despite its simplicity, PCA-VAE exceeds VQ-GAN and SimVQ in reconstruction quality on CelebAHQ while using 10-100x fewer latent bits. It also produces naturally interpretable dimensions (e.g., pose, lighting, gender cues) without adversarial regularization or disentanglement objectives. These results suggest that PCA is a viable replacement for VQ: mathematically grounded, stable, bit-efficient, and semantically structured, offering a new direction for generative models beyond vector quantization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18904 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PCA-VAE: Differentiable Subspace Quantization without Codebook Collapse Lu, Hao Koyun, Onur C. Guo, Yongxin Zhu, Zhengjie Alili, Abbas Gurcan, Metin Nafi Machine Learning Computer Vision and Pattern Recognition Vector-quantized autoencoders deliver high-fidelity latents but suffer inherent flaws: the quantizer is non-differentiable, requires straight-through hacks, and is prone to collapse. We address these issues at the root by replacing VQ with a simple, principled, and fully differentiable alternative: an online PCA bottleneck trained via Oja's rule. The resulting model, PCA-VAE, learns an orthogonal, variance-ordered latent basis without codebooks, commitment losses, or lookup noise. Despite its simplicity, PCA-VAE exceeds VQ-GAN and SimVQ in reconstruction quality on CelebAHQ while using 10-100x fewer latent bits. It also produces naturally interpretable dimensions (e.g., pose, lighting, gender cues) without adversarial regularization or disentanglement objectives. These results suggest that PCA is a viable replacement for VQ: mathematically grounded, stable, bit-efficient, and semantically structured, offering a new direction for generative models beyond vector quantization. |
| title | PCA-VAE: Differentiable Subspace Quantization without Codebook Collapse |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.18904 |