Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models

• URL: http://arxiv.org/abs/2502.07753v1
• Date: Tue, 11 Feb 2025 18:27:27 GMT
• Title: Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models
• Authors: Stanislav Fort, Jonathan Whitaker,
• Abstract summary: We show that discriminative models inherently contain powerful generative capabilities.<n>Our method, Direct Ascent Synthesis, reveals these latent capabilities.<n>DAS achieves high-quality image synthesis by decomposing optimization across multiple spatial scales.
• Score: 6.501811946908292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate that discriminative models inherently contain powerful generative capabilities, challenging the fundamental distinction between discriminative and generative architectures. Our method, Direct Ascent Synthesis (DAS), reveals these latent capabilities through multi-resolution optimization of CLIP model representations. While traditional inversion attempts produce adversarial patterns, DAS achieves high-quality image synthesis by decomposing optimization across multiple spatial scales (1x1 to 224x224), requiring no additional training. This approach not only enables diverse applications -- from text-to-image generation to style transfer -- but maintains natural image statistics ($1/f^2$ spectrum) and guides the generation away from non-robust adversarial patterns. Our results demonstrate that standard discriminative models encode substantially richer generative knowledge than previously recognized, providing new perspectives on model interpretability and the relationship between adversarial examples and natural image synthesis.

Related papers

• Boosting Generative Image Modeling via Joint Image-Feature Synthesis [10.32324138962724]
  We introduce a novel generative image modeling framework that seamlessly bridges the gap by leveraging a diffusion model to jointly model low-level image latents. Our latent-semantic diffusion approach learns to generate coherent image-feature pairs from pure noise. By eliminating the need for complex distillation objectives, our unified design simplifies training and unlocks a powerful new inference strategy: Representation Guidance.
  arXiv  Detail & Related papers  (2025-04-22T17:41:42Z)
• D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens [80.75893450536577]
  We propose D2C, a novel two-stage method to enhance model generation capacity. In the first stage, the discrete-valued tokens representing coarse-grained image features are sampled by employing a small discrete-valued generator. In the second stage, the continuous-valued tokens representing fine-grained image features are learned conditioned on the discrete token sequence.
  arXiv  Detail & Related papers  (2025-03-21T13:58:49Z)
• Nested Diffusion Models Using Hierarchical Latent Priors [23.605302440082994]
  We introduce nested diffusion models, an efficient and powerful hierarchical generative framework.<n>Our approach employs a series of diffusion models to progressively generate latent variables at different semantic levels.<n>To construct these latent variables, we leverage a pre-trained visual encoder, which learns strong semantic visual representations.
  arXiv  Detail & Related papers  (2024-12-08T16:13:39Z)
• A Simple Approach to Unifying Diffusion-based Conditional Generation [63.389616350290595]
  We introduce a simple, unified framework to handle diverse conditional generation tasks. Our approach enables versatile capabilities via different inference-time sampling schemes. Our model supports additional capabilities like non-spatially aligned and coarse conditioning.
  arXiv  Detail & Related papers  (2024-10-15T09:41:43Z)
• Can Generative Models Improve Self-Supervised Representation Learning? [0.7999703756441756]
  We introduce a framework that enriches the self-supervised learning (SSL) paradigm by utilizing generative models to produce semantically consistent image augmentations.<n>Our results show that our framework significantly enhances the quality of learned visual representations by up to 10% Top-1 accuracy in downstream tasks.
  arXiv  Detail & Related papers  (2024-03-09T17:17:07Z)
• UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning [86.91893533388628]
  This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC) UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
  arXiv  Detail & Related papers  (2023-06-01T15:39:38Z)
• Diversity vs. Recognizability: Human-like generalization in one-shot generative models [5.964436882344729]
  We propose a new framework to evaluate one-shot generative models along two axes: sample recognizability vs. diversity. We first show that GAN-like and VAE-like models fall on opposite ends of the diversity-recognizability space. In contrast, disentanglement transports the model along a parabolic curve that could be used to maximize recognizability.
  arXiv  Detail & Related papers  (2022-05-20T13:17:08Z)
• IMAGINE: Image Synthesis by Image-Guided Model Inversion [79.4691654458141]
  We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations. IMAGINE enables the synthesis procedure to simultaneously 1) enforce semantic specificity constraints during the synthesis, 2) produce realistic images without generator training, and 3) give users intuitive control over the generation process.
  arXiv  Detail & Related papers  (2021-04-13T02:00:24Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• High-Fidelity Synthesis with Disentangled Representation [60.19657080953252]
  We propose an Information-Distillation Generative Adrial Network (ID-GAN) for disentanglement learning and high-fidelity synthesis. Our method learns disentangled representation using VAE-based models, and distills the learned representation with an additional nuisance variable to the separate GAN-based generator for high-fidelity synthesis. Despite the simplicity, we show that the proposed method is highly effective, achieving comparable image generation quality to the state-of-the-art methods using the disentangled representation.
  arXiv  Detail & Related papers  (2020-01-13T14:39:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.