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arxiv: 2605.12122 · v1 · submitted 2026-05-12 · 💻 cs.LG · cs.AI· cs.CV

Recognition: no theorem link

Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning

Dong-Jun Han, Hangyeol Jung, Heechan Yun, Hyeonjin Kim, Sungjun Yun

Pith reviewed 2026-05-13 05:42 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CV
keywords concept unlearningsparse autoencodersdiffusion modelscontrastive learningdisentangled representationstext-to-image generationmachine unlearninglatent space separation
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The pith

SAEParate clusters diffusion model latents by concept using contrastive objectives and GeLU to enable precise unlearning without broad interference.

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

The paper proposes SAEParate to fix a key flaw in sparse autoencoder methods for removing unwanted concepts from text-to-image diffusion models. Prior SAEs rely on sparse reconstruction alone, so latent features end up shared across different concepts and suppression of one concept leaks into others. SAEParate adds a concept-aware contrastive objective that pulls same-concept latents together and pushes different-concept ones apart, then adds a GeLU nonlinearity in the encoder to support richer separation. This produces more isolated clusters that let the model suppress a target concept more cleanly while leaving non-target generation intact. The approach is tested on UnlearnCanvas and shows its biggest gains on hard cases that combine style and object unlearning.

Core claim

SAEParate organizes latent representations into concept-specific clusters via a concept-aware contrastive objective, enabling more precise concept suppression while reducing unintended interference during unlearning. In addition, the encoder is enhanced with a GeLU-based nonlinear transformation to increase its expressive capacity under this separation objective, enabling a more discriminative and disentangled latent space.

What carries the argument

SAEParate, a sparse autoencoder that adds a concept-aware contrastive objective to cluster latents by concept and a GeLU nonlinearity to support the clustering, so that target features can be zeroed out independently of others.

If this is right

  • Joint unlearning of style and object pairs becomes more reliable because interference between the two targets drops.
  • Suppression can be performed with lightweight feature edits that leave the diffusion model parameters unchanged.
  • Performance gains are largest on the hardest cases where prior sparse methods show the most leakage.
  • The same latent space remains usable for normal generation after the concept-specific edits are applied.

Where Pith is reading between the lines

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

  • The separation technique could be tested on video diffusion models where temporal consistency makes concept leakage especially costly.
  • If the clusters remain stable across different prompt styles, the method might support selective editing rather than full removal of a concept.
  • The contrastive term might be combined with other regularizers to handle cases where a single image contains multiple target concepts at once.

Load-bearing premise

That the contrastive objective together with GeLU will create clusters separate enough to suppress one concept without lowering reconstruction quality or creating new side effects on other concepts.

What would settle it

Run the unlearned model on prompts that combine the target concept with non-target concepts and check whether the target still appears in generated images or whether non-target quality drops measurably below the original model.

Figures

Figures reproduced from arXiv: 2605.12122 by Dong-Jun Han, Hangyeol Jung, Heechan Yun, Hyeonjin Kim, Sungjun Yun.

Figure 1
Figure 1. Figure 1: Qualitative comparison of joint combination unlearning results. Unlearning results for the target style-object combinations Dogs in Cartoon style (left) and Frogs in Watercolor style (right). First column: target combinations to be removed. Other columns: non-target combinations sharing either the same object or style. SAEParate selectively erases the target while preserving non-target concepts, while SAeU… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of SAE latent representations. (a) UMAP [ [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of SAEParate. We perform concept unlearning in text-to-image diffusion models by suppressing concept-specific features in a Sparse Autoencoder (SAE). (a) Training. A concept￾aware contrastive loss is applied to normalized SAE latents, pulling together features sharing the same label and pushing apart those with different labels. This disentangles object and style information into distinct, sparsel… view at source ↗
Figure 4
Figure 4. Figure 4: Class-wise overlap analysis comparing Vanilla SAE (left) and SAEParate (right). [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Concept-wise k-sparse probing comparison on UnlearnCanvas object concepts. Concept-wise k-sparse Probing. To assess how compactly concept-discriminative information is encoded in the SAE latent space, we perform concept-wise k-sparse probing [15]. For each concept, we select the top-k latents according to the feature-importance score used for unlearning, and train a linear probe with cross-entropy loss to … view at source ↗
Figure 6
Figure 6. Figure 6: Cross-attention score visualization of object classes. [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Full class-wise overlap counts of selected feature groups for object unlearning. [PITH_FULL_IMAGE:figures/full_fig_p023_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Full class-wise maximum cosine similarity between selected feature groups for object [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: UMAP visualization of sparse SAE latent representations for nudity unlearning: Vanilla [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Sequential object unlearning results across increasing numbers of target concepts. [PITH_FULL_IMAGE:figures/full_fig_p027_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative visualization of object sequential unlearning for Vanilla SAE (SAeUron). The [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative visualization of object sequential unlearning for SAEParate (Ours). The rows [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: UMAP visualization of sparse SAE latent representations for style unlearning: Vanilla [PITH_FULL_IMAGE:figures/full_fig_p029_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: UMAP visualization of sparse SAE latent representations for style-object joint unlearning: [PITH_FULL_IMAGE:figures/full_fig_p030_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: UMAP visualization of sparse SAE latent representations for Vanilla SAE (SAeUron) [PITH_FULL_IMAGE:figures/full_fig_p031_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Detailed performance heatmap of SAeUron in object unlearning. [PITH_FULL_IMAGE:figures/full_fig_p032_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Detailed performance heatmap of SAEParate in object unlearning. [PITH_FULL_IMAGE:figures/full_fig_p033_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Detailed performance heatmap of SAeUron in style unlearning. [PITH_FULL_IMAGE:figures/full_fig_p034_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Detailed performance heatmap of SAEParate in style unlearning. [PITH_FULL_IMAGE:figures/full_fig_p035_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Qualitative comparison with Vanilla SAE on object unlearning. [PITH_FULL_IMAGE:figures/full_fig_p037_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Qualitative comparison with Vanilla SAE on style unlearning. [PITH_FULL_IMAGE:figures/full_fig_p038_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Qualitative comparison with Vanilla SAE on style-object joint unlearning. [PITH_FULL_IMAGE:figures/full_fig_p039_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Qualitative comparison with Vanilla SAE on style-object joint unlearning. [PITH_FULL_IMAGE:figures/full_fig_p040_23.png] view at source ↗
read the original abstract

Unlearning specific concepts in text-to-image diffusion models has become increasingly important for preventing undesirable content generation. Among prior approaches, sparse autoencoder (SAE)-based methods have attracted attention due to their ability to suppress target concepts through lightweight manipulation of latent features, without modifying model parameters. However, SAEs trained with sparse reconstruction objectives do not explicitly enforce concept-wise separation, resulting in shared latent features across concepts. To address this, we propose SAEParate, which organizes latent representations into concept-specific clusters via a concept-aware contrastive objective, enabling more precise concept suppression while reducing unintended interference during unlearning. In addition, we enhance the encoder with a GeLU-based nonlinear transformation to increase its expressive capacity under this separation objective, enabling a more discriminative and disentangled latent space. Experiments on UnlearnCanvas demonstrate state-of-the-art performance, with particularly strong gains in joint style-object unlearning, a challenging setting where existing methods suffer from severe interference between target and non-target concepts.

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 paper introduces SAEParate, a variant of sparse autoencoders for unlearning specific concepts in text-to-image diffusion models. Standard SAEs trained only on sparse reconstruction fail to separate features across concepts, causing interference during suppression. SAEParate adds a concept-aware contrastive objective to organize latents into concept-specific clusters and replaces the encoder activation with GeLU to increase expressive capacity under the separation objective. The resulting disentangled space is claimed to enable more precise target-concept suppression with reduced unintended effects on non-target concepts. Experiments on the UnlearnCanvas benchmark report state-of-the-art performance, with the largest gains in the joint style-object unlearning setting.

Significance. If the empirical gains prove robust, the work is significant because it directly targets the interference problem that limits practical deployment of SAE-based unlearning. The contrastive clustering plus nonlinear encoder is a natural, falsifiable extension of existing sparse-representation unlearning literature and could influence subsequent representation-learning approaches for controllable generation. The manuscript supplies a concrete benchmark evaluation, which is a positive attribute.

major comments (2)
  1. [§4] §4 (Experiments): the abstract and results claim state-of-the-art performance with particular gains in joint style-object unlearning, yet no numerical metrics, baseline tables, variance estimates, or statistical tests are referenced. Without these, it is impossible to verify that the reported improvements are attributable to the contrastive objective and GeLU rather than implementation details or benchmark variance. This is load-bearing for the central claim.
  2. [§3.2] §3.2 (Contrastive objective): the construction of positive/negative pairs for the concept-aware contrastive loss is not specified in sufficient detail. If pairs are formed from imperfect concept labels or if the contrastive weight is not ablated against the reconstruction term, the claimed disentanglement may not hold and could degrade sparse reconstruction fidelity. This directly affects the weakest assumption identified in the method.
minor comments (2)
  1. The abstract would be strengthened by including one or two key quantitative results (e.g., FID or unlearning success rates) to support the SOTA claim.
  2. [§2] Notation for the encoder transformation and contrastive loss terms should be introduced once and used consistently across equations and text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and for identifying areas where additional clarity and rigor would strengthen the paper. We address each major comment below and have revised the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [§4] §4 (Experiments): the abstract and results claim state-of-the-art performance with particular gains in joint style-object unlearning, yet no numerical metrics, baseline tables, variance estimates, or statistical tests are referenced. Without these, it is impossible to verify that the reported improvements are attributable to the contrastive objective and GeLU rather than implementation details or benchmark variance. This is load-bearing for the central claim.

    Authors: We agree that the central claims require stronger empirical support. While §4 contains baseline comparison tables on the UnlearnCanvas benchmark, the main text does not explicitly reference the numerical values, report standard deviations across multiple runs, or include statistical tests. In the revised version we have added inline references to the tables, included variance estimates (mean ± std over 5 random seeds), and performed paired t-tests to establish statistical significance of the gains, particularly in the joint style-object setting. We have also inserted a dedicated ablation table isolating the contributions of the contrastive term and GeLU nonlinearity. revision: yes

  2. Referee: [§3.2] §3.2 (Contrastive objective): the construction of positive/negative pairs for the concept-aware contrastive loss is not specified in sufficient detail. If pairs are formed from imperfect concept labels or if the contrastive weight is not ablated against the reconstruction term, the claimed disentanglement may not hold and could degrade sparse reconstruction fidelity. This directly affects the weakest assumption identified in the method.

    Authors: We thank the referee for this observation. Positive pairs are constructed from latent activations sharing the same concept label (style or object) drawn from the UnlearnCanvas training set, while negative pairs are drawn from different concepts; we have now spelled out this procedure, including the source of the labels, in the revised §3.2. We have also added an ablation study varying the contrastive loss weight λ against the reconstruction term and report its effects on both cluster purity (disentanglement) and reconstruction MSE. The results show that moderate λ values improve separation without materially harming fidelity. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's central contribution is the SAEParate architecture, which augments standard sparse autoencoders with a new concept-aware contrastive objective and a GeLU nonlinearity in the encoder to promote concept-specific clustering. This is framed as an additive empirical improvement over prior SAE unlearning work, evaluated on the external UnlearnCanvas benchmark. No equations or claims reduce by construction to fitted inputs, self-definitions, or self-citation chains; the method introduces independent architectural choices whose effectiveness is assessed through standard reconstruction and unlearning metrics rather than tautological renaming or imported uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method extends standard SAE and contrastive learning frameworks whose details are not supplied here.

pith-pipeline@v0.9.0 · 5482 in / 1207 out tokens · 98221 ms · 2026-05-13T05:42:56.486518+00:00 · methodology

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  47. [47]

    Flower blooming in a snow-covered landscape

    “Flower blooming in a snow-covered landscape.”

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  48. [48]

    Frog whose croak sounds like a jazz trumpet

    “Frog whose croak sounds like a jazz trumpet.”

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  49. [49]

    Wild horse galloping across the prairie at sunrise

    “Wild horse galloping across the prairie at sunrise.”

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  50. [50]

    Man hiking through a dense forest

    “Man hiking through a dense forest.” 18

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  51. [51]

    Jellyfish floating in deep blue water

    “Jellyfish floating in deep blue water.”

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  52. [52]

    Rabbit peering out from a burrow

    “Rabbit peering out from a burrow.”

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  53. [53]

    Classic BLT sandwich on toasted bread

    “Classic BLT sandwich on toasted bread.”

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  54. [54]

    Sea waves crashing over ancient coastal ruins

    “Sea waves crashing over ancient coastal ruins.”

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  55. [55]

    Forgotten hero statue covered in ivy

    “Forgotten hero statue covered in ivy.”

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  56. [56]

    Tower soaring above the clouds

    “Tower soaring above the clouds.”

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  57. [57]

    Majestic oak tree in a serene forest

    “Majestic oak tree in a serene forest.”

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    Moonlit waterfall in a serene forest

    “Moonlit waterfall in a serene forest.” For object unlearning, we use the full set of 80anchor promptsfor each object class without the style postfix. The joint unlearning setting uses the same 80anchor promptsfor each (object, style) combination, with the corresponding style postfix appended. At evaluation time, we follow the UnlearnCanvas prompt format ...

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