Diffusion-aware Multi-view Contrastive Learning

• DMCL is a framework that integrates generative diffusion processes with contrastive learning to produce semantically consistent and robust multi-view representations.
• It leverages stochastic reverse diffusion to mitigate noise and impute missing data, enhancing diversity and discriminability in view generation.
• DMCL improves multimodal tasks such as clustering, recommendation, and retrieval by aligning latent representations using InfoNCE-based contrastive objectives.

Diffusion-aware Multi-view Contrastive Learning (DMCL) refers to a class of frameworks that leverage generative diffusion processes for generating or augmenting multi-view representations, subsequently optimized under contrastive learning objectives. DMCL is motivated by the limitations of traditional augmentation or view-generation techniques, which can produce semantically inconsistent or low-diversity samples and are often not robust to corrupted or incomplete data. By integrating diffusion-based sampling into the multi-view pipeline, DMCL enables structured, context-consistent variant generation and jointly aligns the resulting representations, leading to enhanced discriminability and robustness across multimodal learning, clustering, recommendation, and retrieval tasks.

1. Foundational Principles and Motivation

Standard multi-view contrastive learning presumes the availability of semantically aligned "views"—distinct but coherent representations (e.g., modalities, augmentations, partial observations) of the same underlying object. Traditional methods typically employ random augmentations or predefined transformations, which can disrupt essential semantic structure, fail under noise or missingness, or lack diversity (Song et al., 2 Jan 2025, Zhang et al., 28 Jan 2026). DMCL addresses these deficiencies by substituting or supplementing naïve view augmentation with a forward-reverse diffusion process: latent or embedding-level features are stochastically corrupted via a Markovian noise process, then denoised (either unconditionally or conditionally on available views) to synthesize variants that retain core semantics. These generative “views” are then contrastively aligned to enforce a semantically consistent latent space.

Key motivations:

• Semantic consistency: Diffusion-based views are generated by a model trained to preserve data manifold structure, in contrast to random masking or dropout.
• Robustness to noise/incompleteness: Conditioning the denoising process on available data enables principled completion and denoising, enhancing robustness to missing values or noisy modalities (Zhu et al., 11 Sep 2025, Fang, 2023, Zhang et al., 12 Mar 2025).
• Diversity and discriminability: The stochasticity of diffusion reverse sampling injects diversity among generated views, while contrastive losses enforce inter-view alignment and intra-class separation.

2. Diffusion Processes in Multi-View Learning

All current DMCL frameworks adopt discrete-time diffusion models akin to Denoising Diffusion Probabilistic Models (DDPMs). The general structure follows:

• Forward process (noising): Starting with a clean feature or embedding $z_0 \in \mathbb{R}^d$, iteratively apply Gaussian noise:

$$q(z_t|z_{t-1}) = \mathcal{N}(z_t; \sqrt{1-\beta_t} z_{t-1}, \beta_t I)$$

with a schedule $\{\beta_t\}_{t=1}^T$ (often linear or $\sqrt{\cdot}$).

• Closed-form transition: The marginal $q(z_t|z_0)$ is

$$q(z_t|z_0) = \mathcal{N}(z_t; \sqrt{\bar\alpha_t}z_0, (1-\bar\alpha_t)I)$$

where $\bar\alpha_t = \prod_{s=1}^t (1-\beta_s)$.

• Reverse process (denoising): A neural network $\epsilon_\theta$ predicts the noise added at each step (possibly conditioning on context $c$, e.g., other views), parameterizing the mean of the reverse kernel as

$$\mu_\theta(z_t, c, t) = \frac{1}{\sqrt{\alpha_t}}\left[z_t - \frac{1-\alpha_t}{\sqrt{1-\bar\alpha_t}}\epsilon_\theta(z_t, c, t)\right]$$

The reverse process gradually reconstructs plausible view candidates from noisy latents.

Conditional variants (e.g., (Fang, 2023, Zhang et al., 12 Mar 2025)) incorporate cross-attention between available view codes and the denoiser to enable informed imputation and multi-modal fusion. Stochastic fusion (e.g., averaging over denoising chains (Zhu et al., 11 Sep 2025)) further enhances representation quality under uncertainty.

3. Contrastive and Alignment Objectives

DMCL frameworks universally employ contrastive objectives to enforce view consistency and discriminability. The precise forms depend on the application:

• InfoNCE (instance-level) loss: For each sample $i$, treat (fused, view-specific) pairs as positives; all other pairs in the batch are negatives:

$$L_{CL} = -\frac{1}{2N}\sum_{i=1}^N\sum_{m=1}^M \log \frac{\exp(\mathrm{sim}(\hat h_i, h_i^m)/\tau)} {\sum_{j=1}^N \exp(\mathrm{sim}(\hat h_i, h_j^m)/\tau) - \exp(1/\tau)}$$

where $\mathrm{sim}$ denotes cosine similarity, $\tau$ is a temperature, and $(\hat h_i, h_i^m)$ is a fused–view pair (Zhu et al., 11 Sep 2025).

• Multi-positive, multi-view contrast: In complex setups, symmetric InfoNCE or multi-view variants handle multiple positives, as in

$$\mathcal{L}_{\mathrm{NCE}}(U,V;\tau) = -\frac{1}{2N} \left[\sum_{i,j} \tilde q_{i,j}\log p_{i,j} + \sum_{i,j} \tilde q_{j,i}\log p_{j,i}\right]$$

with label smoothing, and $p_{i,j}$ as normalized similarities (Zhang et al., 28 Jan 2026).

• Category-level and clustering objectives: Soft assignments from projection heads (e.g., via softmax) across views are also contrastively aligned (Zhang et al., 12 Mar 2025, Fang, 2023).
• Additional regularizations: Jensen-Shannon distribution alignment between retrieval distributions, or entropy regularization to prevent collapse.

The integration of diffusion-generated and real (or observable) views within these contrastive losses aligns the generative process with discriminative learning, ensuring that diversity induced by stochastic denoising yields informative and class-consistent variations.

4. Representative Architectures and Methodological Variants

The DMCL paradigm is instantiated across several model classes:

Application Domain	Reference	Diffusion Conditioning/Fusion	Contrastive Loss Type	View Handling
Multimodal recommendation	(Song et al., 2 Jan 2025)	Independent per-modality, graph	User/item InfoNCE	Visual, textual
Multi-view clustering	(Zhu et al., 11 Sep 2025)	B chains, context cat(z₁,…,zₘ)	Fused–view InfoNCE	Robust to missing
Incomplete MVC	(Fang, 2023)	Cross-attn on available views	Spectral + category contrast	Diff completion
Incomplete MVC, general	(Zhang et al., 12 Mar 2025)	Independent, contrast/MI fusion	Inter-, intra-view cl/inter.	Arbitrary missing
DAI Text-to-Image Retrieval	(Zhang et al., 28 Jan 2026)	Text and diffusion query fusion	Symmetric InfoNCE, HNM	Multi-view queries

• Editor's term: Table represents core architectural differences in published DMCL frameworks.

Most pipelines follow a staged training protocol:

1. Pre-train per-view autoencoders for representation extraction.
2. Train diffusion models (conditional or unconditional) in latent space, with explicit noise prediction.
3. Contrastive fine-tuning to enforce multi-view alignment, often with joint clustering or recommendation losses.
4. During inference, missing views are imputed via diffusion denoising chains (possibly averaging multiple runs for robustness).

Specific advancements include the use of item–item graph augmentation and stable ID embeddings in multimodal recommendation (Song et al., 2 Jan 2025), stochastic ensemble fusion via multiple denoising chains (Zhu et al., 11 Sep 2025), and hallucination suppression in retrieval (Zhang et al., 28 Jan 2026).

5. Handling Noisy, Missing, and Hallucinated Data

A core strength of DMCL is robustness to imperfect views, whether due to data corruption, incompleteness, or generative hallucination.

• Noisy/missing views: Conditional diffusion enables imputation of missing or corrupted modalities by leveraging the available ones, with cross-attention facilitating pattern completion (Fang, 2023, Zhang et al., 12 Mar 2025).
• Multi-view fusion under low quality: Multiple stochastic reverse chains and averaging (SGDF) stabilize feature fusion, mitigating individual view noise (Zhu et al., 11 Sep 2025).
• Hallucination suppression: In diffusion-augmented retrieval, DMCL explicitly aligns diffusion-generated proxies with text queries, enforcing semantic filtering and suppressing spurious variation via consistency and distribution matching losses (Zhang et al., 28 Jan 2026). Empirical visualizations and geometric analyses confirm that learned embeddings prioritize intent-consistent features and map hallucinated content to subspaces with minimal discriminative power.

6. Empirical Performance and Benchmarking

Across tasks and datasets, DMCL-based models have demonstrated consistent improvements over baselines that use non-generative or random augmentation schemes.

• Multimodal recommendation (Song et al., 2 Jan 2025): DMCL (DiffCL) achieves up to 3–65% relative gains in Recall@10/20 and NDCG@10/20 over established baselines, with all ablated variants showing significant performance drops.
• Multi-view clustering (Zhu et al., 11 Sep 2025): GDCN with DMCL attains, e.g., 0.902→0.980 accuracy on NGs, with removal of the diffusion or contrastive component reducing accuracy by up to 36 percentage points.
• Incomplete multi-view clustering (Fang, 2023, Zhang et al., 12 Mar 2025): DMCL frameworks achieve marked improvements in ACC/NMI/ARI, with robustness even at high missing rates (e.g., 70% missing views, up to 10pp gain over strong baselines).
• Text-to-image retrieval (Zhang et al., 28 Jan 2026): DMCL increases cumulative Hits@10 by 4-8 percentage points over prior diffusion-augmented and zero-shot baselines across five benchmarks.

Ablation experiments consistently confirm the necessity of both diffusion-driven augmentation and contrastive alignment. Hyperparameter sensitivity analyses indicate the improvements are robust to reasonable $\lambda$ and schedule choices.

7. Theoretical Insights and Future Directions

Analysis highlights several theoretical properties:

• Convergence: The diffusion process under mild Lipschitz continuity guarantees weak convergence to the true data manifold, ensuring semantic consistency in generated views (Fang, 2023).
• Consistency bounds: Conditioning the denoising process on observed views tightens the multi-view consistency bound, theoretically grounding the improvement in alignment and imputation accuracy.
• Cluster recovery: Spectral contrastive losses provide provable guarantees of cluster recovery under suitable view alignment assumptions.

Open research avenues:

• Extension to higher-order or more complex view topologies, with learned rather than heuristic conditioning.
• Integration with large-scale pretrained models (e.g., diffusion models conditioned on LLM-derived context (Zhang et al., 28 Jan 2026)).
• Exploration of optimal contrastive objectives for highly imbalanced or asymmetric multi-view data.

In summary, Diffusion-aware Multi-view Contrastive Learning (DMCL) constitutes a broadly applicable paradigm for robust, semantically faithful, and discriminative representation learning in multi-view, multimodal, and incomplete or generative data regimes, grounded in the synergy between generative diffusion modeling and contrastive self-supervision (Song et al., 2 Jan 2025, Zhu et al., 11 Sep 2025, Fang, 2023, Zhang et al., 12 Mar 2025, Zhang et al., 28 Jan 2026).