Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task

• URL: http://arxiv.org/abs/2310.09336v4
• Date: Fri, 16 Feb 2024 22:22:59 GMT
• Title: Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
• Authors: Maya Okawa, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka
• Abstract summary: We study compositional generalization in conditional diffusion models in a synthetic setting. We find that the order in which the ability to generate samples emerges is governed by the structure of the underlying data-generating process. Our study lays a foundation for understanding capabilities and compositionality in generative models from a data-centric perspective.
• Score: 20.749514363389878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern generative models exhibit unprecedented capabilities to generate extremely realistic data. However, given the inherent compositionality of the real world, reliable use of these models in practical applications requires that they exhibit the capability to compose a novel set of concepts to generate outputs not seen in the training data set. Prior work demonstrates that recent diffusion models do exhibit intriguing compositional generalization abilities, but also fail unpredictably. Motivated by this, we perform a controlled study for understanding compositional generalization in conditional diffusion models in a synthetic setting, varying different attributes of the training data and measuring the model's ability to generate samples out-of-distribution. Our results show: (i) the order in which the ability to generate samples from a concept and compose them emerges is governed by the structure of the underlying data-generating process; (ii) performance on compositional tasks exhibits a sudden "emergence" due to multiplicative reliance on the performance of constituent tasks, partially explaining emergent phenomena seen in generative models; and (iii) composing concepts with lower frequency in the training data to generate out-of-distribution samples requires considerably more optimization steps compared to generating in-distribution samples. Overall, our study lays a foundation for understanding capabilities and compositionality in generative models from a data-centric perspective.

Related papers

• Dynamics of Concept Learning and Compositional Generalization [23.43600409313907]
  We introduce a structured identity mapping (SIM) task, where a model is trained to learn the identity mapping on a Gaussian mixture with structurally organized centroids. We mathematically analyze the learning dynamics of neural networks trained on this SIM task and show that, despite its simplicity, SIM's learning dynamics capture and help explain key empirical observations. Our theory also offers several new insights -- e.g., we find a novel mechanism for non-monotonic learning dynamics of test loss in early phases of training.
  arXiv  Detail & Related papers  (2024-10-10T18:58:29Z)
• How Diffusion Models Learn to Factorize and Compose [14.161975556325796]
  Diffusion models are capable of generating photo-realistic images that combine elements which likely do not appear together in the training set. We investigate whether and when diffusion models learn semantically meaningful and factorized representations of composable features.
  arXiv  Detail & Related papers  (2024-08-23T17:59:03Z)
• The Extrapolation Power of Implicit Models [2.3526338188342653]
  Implicit models are put to the test across various extrapolation scenarios: out-of-distribution, geographical, and temporal shifts. Our experiments consistently demonstrate significant performance advantage with implicit models.
  arXiv  Detail & Related papers  (2024-07-19T16:01:37Z)
• Provable Statistical Rates for Consistency Diffusion Models [87.28777947976573]
  Despite the state-of-the-art performance, diffusion models are known for their slow sample generation due to the extensive number of steps involved. This paper contributes towards the first statistical theory for consistency models, formulating their training as a distribution discrepancy minimization problem.
  arXiv  Detail & Related papers  (2024-06-23T20:34:18Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory [87.00653989457834]
  Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning. Despite the empirical success, theory of conditional diffusion models is largely missing. This paper bridges the gap by presenting a sharp statistical theory of distribution estimation using conditional diffusion models.
  arXiv  Detail & Related papers  (2024-03-18T17:08:24Z)
• On the Limitation of Diffusion Models for Synthesizing Training Datasets [5.384630221560811]
  This paper investigates the gap between synthetic and real samples by analyzing the synthetic samples reconstructed from real samples through the diffusion and reverse process. We found that the synthetic datasets degrade classification performance over real datasets even when using state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T01:42:23Z)
• Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models [77.83923746319498]
  We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models. We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
  arXiv  Detail & Related papers  (2023-05-29T04:22:57Z)
• Diffusing Gaussian Mixtures for Generating Categorical Data [21.43283907118157]
  We propose a generative model for categorical data based on diffusion models with a focus on high-quality sample generation. Our method of evaluation highlights the capabilities and limitations of different generative models for generating categorical data.
  arXiv  Detail & Related papers  (2023-03-08T14:55:32Z)
• Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC [102.64648158034568]
  diffusion models have quickly become the prevailing approach to generative modeling in many domains. We propose an energy-based parameterization of diffusion models which enables the use of new compositional operators. We find these samplers lead to notable improvements in compositional generation across a wide set of problems.
  arXiv  Detail & Related papers  (2023-02-22T18:48:46Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)

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.