title: Direct Discriminative Optimization: Your Likelihood-Based Visual
    Generative Model is Secretly a GAN Discriminator
summary: While likelihood-based generative models, particularly diffusion and
  autoregressive models, have achieved remarkable fidelity in visual generation,
  the maximum likelihood estimation (MLE) objective inherently suffers from a
  mode-covering tendency that limits the generation quality under limited model
  capacity. In this work, we propose Direct Discriminative Optimization (DDO) as
  a unified framework that bridges likelihood-based generative training and the
  GAN objective to bypass this fundamental constraint. Our key insight is to
  parameterize a discriminator implicitly using the likelihood ratio between a
  learnable target model and a fixed reference model, drawing parallels with the
  philosophy of Direct Preference Optimization (DPO). Unlike GANs, this
  parameterization eliminates the need for joint training of generator and
  discriminator networks, allowing for direct, efficient, and effective
  finetuning of a well-trained model to its full potential beyond the limits of
  MLE. DDO can be performed iteratively in a self-play manner for progressive
  model refinement, with each round requiring less than 1% of pretraining epochs.
  Our experiments demonstrate the effectiveness of DDO by significantly advancing
  the previous SOTA diffusion model EDM, reducing FID scores from 1.79/1.58 to
  new records of 1.30/0.97 on CIFAR-10/ImageNet-64 datasets, and by consistently
  improving both guidance-free and CFG-enhanced FIDs of visual autoregressive
  models on ImageNet 256times256.
paper_authors._id: 67c6d1c35e896ed915374027
paper_authors.name: Kaiwen Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T10:17:24.142Z
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paper_authors.fullname: Kaiwen Zheng
paper_authors.type: user
paper_authors.user: worstcoder
paper_authors._id: 67c6d1c35e896ed915374028
paper_authors.name: Yongxin Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T11:16:59.170Z
paper_authors._id: 66f4cf1a03b5ba8a7f1f6522
paper_authors.avatarUrl: /avatars/2768d6e37d3f280194cfb8ed274f6015.svg
paper_authors.fullname: Yongxin Chen
paper_authors.type: user
paper_authors.user: Ema11
paper_authors._id: 67c6d1c35e896ed915374029
paper_authors.name: Huayu Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: HuayuChen
paper_authors.type: user
paper_authors.user: HuayuChen
paper_authors._id: 67c6d1c35e896ed91537402a
paper_authors.name: Guande He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T11:17:20.266Z
paper_authors._id: 67492ee82ad3cfc108a41bbb
paper_authors.avatarUrl: /avatars/7ad03e55a8791c62f1271a5c9bf8cc60.svg
paper_authors.fullname: Guande He
paper_authors.type: user
paper_authors.user: gdhe17
paper_authors._id: 67c6d1c35e896ed91537402b
paper_authors.name: Ming-Yu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T11:17:27.270Z
paper_authors._id: 62f049afdf4b93aad5c7f2d6
paper_authors.avatarUrl: /avatars/e272e58ad996733d7098e50248e5b57e.svg
paper_authors.fullname: Ming-Yu Liu
paper_authors.type: user
paper_authors.user: mingyuliutw
paper_authors._id: 67c6d1c35e896ed91537402c
paper_authors.name: Jun Zhu
paper_authors._id: 67c6d1c35e896ed91537402d
paper_authors.name: Qinsheng Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T11:17:33.763Z
paper_authors._id: 6732d5dea24987c43bfbafd8
paper_authors.avatarUrl: /avatars/1581373b9de5069975716932fceb976b.svg
paper_authors.fullname: Qinsheng Zhang
paper_authors.type: user
paper_authors.user: qsh-zh
paper_title: Direct Discriminative Optimization: Your Likelihood-Based Visual
    Generative Model is Secretly a GAN Discriminator
paper_summary: While likelihood-based generative models, particularly diffusion and
  autoregressive models, have achieved remarkable fidelity in visual generation,
  the maximum likelihood estimation (MLE) objective inherently suffers from a
  mode-covering tendency that limits the generation quality under limited model
  capacity. In this work, we propose Direct Discriminative Optimization (DDO) as
  a unified framework that bridges likelihood-based generative training and the
  GAN objective to bypass this fundamental constraint. Our key insight is to
  parameterize a discriminator implicitly using the likelihood ratio between a
  learnable target model and a fixed reference model, drawing parallels with the
  philosophy of Direct Preference Optimization (DPO). Unlike GANs, this
  parameterization eliminates the need for joint training of generator and
  discriminator networks, allowing for direct, efficient, and effective
  finetuning of a well-trained model to its full potential beyond the limits of
  MLE. DDO can be performed iteratively in a self-play manner for progressive
  model refinement, with each round requiring less than 1% of pretraining epochs.
  Our experiments demonstrate the effectiveness of DDO by significantly advancing
  the previous SOTA diffusion model EDM, reducing FID scores from 1.79/1.58 to
  new records of 1.30/0.97 on CIFAR-10/ImageNet-64 datasets, and by consistently
  improving both guidance-free and CFG-enhanced FIDs of visual autoregressive
  models on ImageNet 256times256.
paper_ai_summary: A unified framework called Direct Discriminative Optimization (DDO) enhances generative models by combining likelihood-based and GAN objectives, improving model fidelity and efficiency without joint training.
paper_ai_keywords: likelihood-based generative models diffusion models autoregressive models maximum likelihood estimation (MLE) Direct Discriminative Optimization (DDO) discriminator likelihood ratio Direct Preference Optimization (DPO) generator SOTA diffusion model EDM FID scores guidance-free CFG-enhanced ImageNet-64 ImageNet 256$\times$256