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arxiv: 2605.27495 · v1 · pith:HLEZYL3Qnew · submitted 2026-05-26 · 💻 cs.CV · cs.LG

Representation-Conditioned Diffusion Models for Guided Training Data Generation

Nithesh Chandher Karthikeyan , Jonas Unger , Gabriel Eilertsen This is my paper

Pith reviewed 2026-06-29 18:08 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords diffusion modelssynthetic dataimage classificationrepresentation conditioningDINOCLIPdata augmentationImageNet
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The pith

Conditioning latent diffusion models on DINO and CLIP representations generates synthetic images that train ImageNet classifiers to higher accuracy than real data.

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

The paper evaluates classifiers trained solely on images produced by latent diffusion models whose generation is guided by feature vectors from DINOv2, DINOv3, or CLIP rather than by class labels. On ImageNet100 this representation-conditioned approach raises top-1 accuracy by 10.76 points over class-conditioned baselines; simply increasing the number of generated images then pushes performance 2 points above a model trained on the original real training set. The same conditioning space also supports sample filtering and data augmentation that beat standard augmentation pipelines. A reader would care because the results indicate a concrete route to scale visual training sets without additional human annotation or collection costs.

Core claim

Latent diffusion models conditioned on learned representations from DINOv2, DINOv3, and CLIP produce synthetic images whose quality and mode coverage exceed those of class-conditioned generation, yielding +10.76 p.p. top-1 accuracy on ImageNet100; scaling the synthetic set size further allows a classifier trained only on generated images to surpass one trained on the real data by +2.0 p.p.

What carries the argument

Representation-conditioned latent diffusion, in which the denoising network receives a feature vector extracted by a frozen self-supervised model (DINO or CLIP) instead of a one-hot class label.

If this is right

  • Increasing the volume of representation-conditioned synthetic images produces monotonic gains in downstream classifier accuracy.
  • The same generated set can be filtered by proximity in the conditioning space to remove low-value samples and raise training performance further.
  • Representation-conditioned images serve as effective augmentation data that outperform classical geometric and photometric augmentations.
  • The performance gap over class-conditioned generation arises from both higher per-sample fidelity and better coverage of the real data distribution.

Where Pith is reading between the lines

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

  • If the conditioning mechanism transfers to other modalities, the same approach could generate training data for audio or video tasks where labeled corpora are even scarcer.
  • The method implicitly assumes access to a strong frozen representation model; weaker conditioning signals might narrow or eliminate the observed gains.
  • Because the generated images are produced from the same representation space used for filtering, the pipeline could be closed-loop: generate, filter, retrain the conditioner, and repeat.

Load-bearing premise

Classifiers trained on the generated images will generalize to real test images without harmful distribution shift or mode-specific artifacts that would invalidate the reported accuracy numbers.

What would settle it

Train the reported classifier on a large set of the generated images and evaluate its top-1 accuracy on the real ImageNet100 validation set; if accuracy does not exceed the real-data baseline when the synthetic set is scaled, the central claim is false.

Figures

Figures reproduced from arXiv: 2605.27495 by Gabriel Eilertsen, Jonas Unger, Nithesh Chandher Karthikeyan.

Figure 1
Figure 1. Figure 1: Overall pipeline of the methodology, which consist of two parts: (a) Synthetic Data Generation using Representation Conditioned [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Scaling synthetic datasets for classifier training. Top-1 (left) and Top-5 (right) accuracy of a ResNet-50 trained on synthetic [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

Data availability remains a critical bottleneck in many deep learning applications. Large-scale datasets are often expensive to collect, curate and annotate, which can limit the scalability and applicability of supervised learning methods. In this work, we evaluate the classification performance of models trained on synthetic image datasets produced by generative deep learning. In particular, we use latent diffusion models conditioned on learned representations from DINOv2, DINOv3, and CLIP. Our results demonstrates that this representation-conditioned formulation significantly outperforms class-conditioned generation by a large margin (+10.76 p.p. top-1 accuracy on ImageNet100), by improving sample quality and mode coverage. Furthermore, by scaling the size of the synthetic dataset, we are able to outperform a classifier trained on the real data (+2.0 p.p top-1 accuracy). We also demonstrate how generated images can be used for augmentation purposes, outperforming classical augmentation methods, and how the conditioning space can be used for sample filtering to further improve training value. Collectively, these findings highlight that representation-conditioned diffusion models provide a promising approach for augmenting, complementing, or potentially replacing real-world datasets in large-scale visual learning tasks.

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 / 2 minor

Summary. The manuscript proposes using latent diffusion models conditioned on learned representations from DINOv2, DINOv3, and CLIP (rather than class labels) to generate synthetic image datasets. It claims this representation-conditioned approach yields synthetic data that, when used to train classifiers on ImageNet100, outperforms class-conditioned generation by +10.76 p.p. top-1 accuracy and, when the synthetic dataset is scaled, outperforms a classifier trained on real data by +2.0 p.p. Additional uses for augmentation and conditioning-based filtering are demonstrated.

Significance. If the empirical gains prove robust, the work would be significant for data-scarce regimes in computer vision, as it suggests a practical route to generating training data whose downstream utility exceeds that of real data in at least one setting. The core idea of leveraging rich representation spaces for conditioning addresses a known limitation of class-conditional generators (mode collapse) and could influence future synthetic-data pipelines.

major comments (2)
  1. [Abstract] Abstract: the headline results (+10.76 p.p. over class-conditioned diffusion and +2.0 p.p. over real data) are presented with no description of experimental protocol, baseline implementations, data splits, training hyperparameters, statistical testing, or controls for confounding factors. Without these details the central empirical claim cannot be evaluated or reproduced.
  2. [Abstract] Abstract: the assertion that the method improves 'sample quality and mode coverage' rests solely on the downstream top-1 accuracy figures; no independent quantitative evidence (FID, precision/recall, per-class diversity, or distribution-shift diagnostics on held-out real data) is supplied to support that the synthetic marginal is close enough to the real marginal to avoid spurious features or harmful shift.
minor comments (2)
  1. [Abstract] Abstract: grammatical error ('Our results demonstrates' should read 'Our results demonstrate').
  2. [Abstract] Abstract: 'DINOv3' is referenced without citation or clarification; if it is a non-standard variant, a pointer to the implementation or paper is needed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback. We agree that the abstract would benefit from greater self-containment and will revise it accordingly while preserving its brevity. Detailed responses to the major comments follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline results (+10.76 p.p. over class-conditioned diffusion and +2.0 p.p. over real data) are presented with no description of experimental protocol, baseline implementations, data splits, training hyperparameters, statistical testing, or controls for confounding factors. Without these details the central empirical claim cannot be evaluated or reproduced.

    Authors: The full experimental protocol, including the ImageNet100 subset, class-conditioned LDM baselines, classifier training hyperparameters, and data splits, is provided in Sections 3 and 4 of the manuscript. No multiple-run statistical testing or explicit confounding controls beyond standard practices were performed. To address the concern, we will expand the abstract with a single sentence summarizing the core setup (dataset, conditioning sources, and evaluation protocol) while keeping the length appropriate for an abstract. revision: yes

  2. Referee: [Abstract] Abstract: the assertion that the method improves 'sample quality and mode coverage' rests solely on the downstream top-1 accuracy figures; no independent quantitative evidence (FID, precision/recall, per-class diversity, or distribution-shift diagnostics on held-out real data) is supplied to support that the synthetic marginal is close enough to the real marginal to avoid spurious features or harmful shift.

    Authors: Downstream accuracy on a held-out real test set is our primary and most application-relevant indicator of sample quality and mode coverage, as it directly measures whether the generated data enables better generalization than alternatives. We recognize that complementary distribution-level metrics would strengthen the claims. In the revision we will add FID, precision/recall, and per-class diversity statistics computed against the real ImageNet100 validation set. revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical comparisons without derivations or self-referential reductions.

full rationale

The paper reports empirical accuracy gains from training classifiers on synthetic images generated by representation-conditioned diffusion models versus class-conditioned baselines and real data. No equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. All claims rest on direct experimental measurements against external baselines, satisfying the condition for a self-contained result with no reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; the work is presented as an empirical evaluation of an existing generative architecture with a new conditioning strategy.

pith-pipeline@v0.9.1-grok · 5742 in / 1241 out tokens · 33643 ms · 2026-06-29T18:08:40.811900+00:00 · methodology

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