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arxiv: 2605.25012 · v1 · pith:Q2UYPSG7new · submitted 2026-05-24 · 💻 cs.CV

Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation

Imanol G. Estepa , Jes\'us M Rodr\'iguez-de-Vera , Bhalaji Nagarajan , Petia Radeva This is my paper

Pith reviewed 2026-06-30 12:25 UTC · model grok-4.3

classification 💻 cs.CV
keywords self-supervised learningcodebook learningunified vision modelmasked image modelingcontrastive learningdiscrete token spaceImageNet-1KVQGAN
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The pith

LEASE unifies visual representation and generation by training a single encoder on semantic codebooks in a precomputed discrete token space.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LEASE, a self-supervised method that aligns generative and discriminative objectives inside one discrete token space. It precomputes a paired generative-discriminative codebook once, then trains an encoder with masked token reconstruction for detail and codebook contrast for semantic alignment. This produces a latent space usable for both high-quality image generation and strong downstream recognition without augmentations or teacher models. On ImageNet-1K the resulting model reports gains over prior unified VQGAN baselines across linear probing, unconditional generation, few-shot, transfer, and robustness tasks while remaining competitive with specialized contrastive and generative models.

Core claim

LEASE shows that a one-time precomputed paired codebook plus the combination of masked reconstruction loss and adaptive codebook contrast loss yields a single latent space that supports both unconditional generation and representation learning at state-of-the-art unified performance levels on ImageNet-1K.

What carries the argument

Paired generative-discriminative codebook with masked token reconstruction and codebook contrast losses operating in a fixed discrete token space.

If this is right

  • Linear probing accuracy improves by up to 1.7 percent over prior VQGAN-based unified models.
  • Unconditional generation reaches lower FID and higher IS than MAGE.
  • Few-shot and transfer performance rise by roughly 0.5 to 0.75 percent on average.
  • Robustness benchmarks improve by 4-6 percent on average over the same baselines.
  • The same unsupervised encoder can be extended to conditional generation while remaining competitive with specialized models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The fixed codebook may allow training on smaller compute budgets than methods that require online tokenization or heavy augmentations.
  • Because the token space is precomputed once, the approach could be applied to new domains by swapping only the codebook construction step.
  • The separation of generative and discriminative codebooks suggests a route to test whether further specialization of the two dictionaries would increase the performance gap.
  • Success on ImageNet raises the question of whether the same dual-objective recipe scales to video or 3-D data without architectural changes.

Load-bearing premise

A single precomputed discrete token space plus the two losses is sufficient to produce a latent representation that is simultaneously good for generation and discrimination without further supervision.

What would settle it

A replication on ImageNet-1K in which the LEASE model fails to exceed MAGE or Sorcen on both linear-probing accuracy and unconditional FID simultaneously.

Figures

Figures reproduced from arXiv: 2605.25012 by Bhalaji Nagarajan, Imanol G. Estepa, Jes\'us M Rodr\'iguez-de-Vera, Petia Radeva.

Figure 1
Figure 1. Figure 1: Linear probing and class-unconditional generation performance. Our approach, LEASE, advances the Unified SSL SoTA and defines a new Pareto-front for unified models. 45, 60]. Trained largely on self-supervised or weakly su￾pervised objectives [6, 41], they learn rich latent represen￾tations that support a wide range of discriminative tasks. However, these representations cannot be directly trans￾lated into … view at source ↗
Figure 2
Figure 2. Figure 2: (Left) [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of LEASE. Visible generative tokens are encoded and placed into a reconstruction canvas for masked token prediction, forming the reconstruction objective. Parallelly, visible discriminative tokens are mapped to their codebook centroids, providing positives and neighbors for the codebook contrastive loss, where visible token embeddings and centroids are pulled together. established a strong baselin… view at source ↗
Figure 4
Figure 4. Figure 4: Training time for VQGAN-based unified methods. Method Learning type FID IS LP% FT% DINOv2 ViT-B - - 84.5 85.7 REPA Distillation - - 68.2 - DiGIT Codebook Input 9.13 73.85 71.7 - SVG Init/Distill - - - 81.8 VFMTok Distillation - - 69.4 - MergeVQ (G+R) Distillation - - 77.9 82.0 LEASE (Ours) Codebook Contrast 9.62 91.78 76.7 82.7 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

Discriminative and generative vision models excel in their respective domains but remain semantically misaligned, hindering progress toward unified visual learning. We introduce LEASE (LEArning from SEmantic Dictionaries), a self-supervised framework that bridges this gap using a paired generative-discriminative codebook design. LEASE operates entirely in a discrete token space produced through a one-time precomputation step, enabling efficient training without data augmentations, teacher models, or online tokenizers. LEASE integrates two complementary objectives: a masked token reconstruction loss that captures fine-grained generative detail, and a codebook contrast loss that aligns encoder features with discriminative semantics via adaptive centroid weighting. This dual supervision yields a unified latent space that supports both high-quality generation and strong representation learning. On ImageNet-1K, LEASE achieves state-of-the-art unified performance, outperforming prior VQGAN-based methods such as MAGE and Sorcen across linear probing (up to +1.7%), unconditional generation (-1.26 FID and +10.19 IS w.r.t MAGE), few-shot learning (+0.56% on average against Sorcen), transfer (+0.75% average improvement against MAGE and Sorcen), and robustness benchmarks (+5.86% and +4.25% average improvement against MAGE and Sorcen, respectively). It also competes favorably with domain-specialized contrastive and generative models while surpassing previous MIM methods. The unsupervised LEASE model can also be extended to conditional generation by building upon its learned representations, proving competitive with specialized baselines. Overall, LEASE provides an efficient and effective step toward general-purpose vision models that jointly understand and generate visual content.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces LEASE, a self-supervised framework for unified visual representation and generation. It relies on a one-time precomputed discrete token space from a VQGAN-style codebook, trained with two objectives: masked token reconstruction for generative detail and codebook contrast loss with adaptive centroid weighting for discriminative alignment. The central claim is that this produces a single latent space supporting both high-quality generation and strong representation learning without data augmentations or teacher models. On ImageNet-1K, it reports SOTA unified results outperforming VQGAN-based baselines (MAGE, Sorcen) on linear probing (+1.7%), unconditional generation (-1.26 FID, +10.19 IS vs MAGE), few-shot (+0.56% avg vs Sorcen), transfer (+0.75% avg), and robustness (+5.86% and +4.25% avg), while competing with specialized models and extending to conditional generation.

Significance. If verified, the result would be significant for efficient unification of generative and discriminative vision models. The approach's avoidance of augmentations, teachers, and online tokenizers, combined with multi-task evaluation on standard ImageNet-1K benchmarks (probing, generation metrics, few-shot, transfer, robustness), provides a concrete step toward general-purpose models. The extension to conditional generation from the learned representations is a noted strength.

major comments (2)
  1. [Method and Experiments] The central claim that the dual objectives produce a unified latent space (abstract) rests on the necessity of the codebook contrast loss; however, no ablation isolates its contribution by removing the contrast term while retaining masked reconstruction on the fixed precomputed codebook. This is load-bearing for the reported margins (+1.7% linear probing, robustness gains) because the precomputation step alone may already encode sufficient semantics, leaving open whether the contrast objective is required or marginal.
  2. [Experiments] §4 (Experiments): the SOTA claims vs MAGE and Sorcen are presented without error bars, multiple runs, or statistical significance tests on the key metrics (FID, IS, linear probing accuracy); this weakens the ability to confirm the unification benefit is robust rather than run-specific.
minor comments (1)
  1. [Abstract/Method] The abstract and method description could more explicitly define the adaptive centroid weighting mechanism and its relation to the codebook contrast loss equation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below and commit to the indicated revisions.

read point-by-point responses

  1. Referee: [Method and Experiments] The central claim that the dual objectives produce a unified latent space (abstract) rests on the necessity of the codebook contrast loss; however, no ablation isolates its contribution by removing the contrast term while retaining masked reconstruction on the fixed precomputed codebook. This is load-bearing for the reported margins (+1.7% linear probing, robustness gains) because the precomputation step alone may already encode sufficient semantics, leaving open whether the contrast objective is required or marginal.

    Authors: We agree that an explicit ablation isolating the codebook contrast loss (while retaining masked reconstruction on the fixed precomputed codebook) is required to substantiate its contribution to the unified latent space and the reported gains. In the revised manuscript we will add this ablation, training the contrast-ablated variant and reporting its linear probing accuracy, robustness metrics, and generation performance relative to the full LEASE model. revision: yes

  2. Referee: [Experiments] §4 (Experiments): the SOTA claims vs MAGE and Sorcen are presented without error bars, multiple runs, or statistical significance tests on the key metrics (FID, IS, linear probing accuracy); this weakens the ability to confirm the unification benefit is robust rather than run-specific.

    Authors: We concur that variability measures would strengthen confidence in the SOTA margins. In the revision we will rerun the primary evaluation protocols (linear probing, unconditional generation FID/IS) over multiple random seeds, report means with standard deviations, and include statistical significance tests against the MAGE and Sorcen baselines. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation self-contained against external benchmarks

full rationale

The paper's central claims rest on a one-time precomputed VQGAN-style codebook plus dual masked-reconstruction and codebook-contrast objectives, with all reported gains (linear probing, FID/IS, few-shot, transfer, robustness) measured against external baselines such as MAGE and Sorcen on ImageNet-1K. No equations, fitted parameters renamed as predictions, self-citation chains, or ansatzes are present in the supplied text that would reduce any result to its own inputs by construction. The method description treats the codebook as an independent preprocessing step and evaluates unification via standard external metrics, satisfying the criteria for a non-circular, self-contained derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides insufficient detail to identify specific free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5863 in / 1033 out tokens · 34383 ms · 2026-06-30T12:25:54.517793+00:00 · methodology

discussion (0)

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