Random Network Distillation as a Diversity Metric for Both Image and Text Generation

• URL: http://arxiv.org/abs/2010.06715v1
• Date: Tue, 13 Oct 2020 22:03:52 GMT
• Title: Random Network Distillation as a Diversity Metric for Both Image and Text Generation
• Authors: Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein
• Abstract summary: We develop a new diversity metric that can be applied to data, both synthetic and natural, of any type. We validate and deploy this metric on both images and text.
• Score: 62.13444904851029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models are increasingly able to produce remarkably high quality images and text. The community has developed numerous evaluation metrics for comparing generative models. However, these metrics do not effectively quantify data diversity. We develop a new diversity metric that can readily be applied to data, both synthetic and natural, of any type. Our method employs random network distillation, a technique introduced in reinforcement learning. We validate and deploy this metric on both images and text. We further explore diversity in few-shot image generation, a setting which was previously difficult to evaluate.

Related papers

• Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model [80.61157097223058]
  A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. We introduce an innovative inter-class data augmentation method known as Diff-Mix, which enriches the dataset by performing image translations between classes.
  arXiv  Detail & Related papers  (2024-03-28T17:23:45Z)
• Active Generation for Image Classification [50.18107721267218]
  We propose to address the efficiency of image generation by focusing on the specific needs and characteristics of the model. With a central tenet of active learning, our method, named ActGen, takes a training-aware approach to image generation.
  arXiv  Detail & Related papers  (2024-03-11T08:45:31Z)
• Instruct-Imagen: Image Generation with Multi-modal Instruction [90.04481955523514]
  instruct-imagen is a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision. Human evaluation on various image generation datasets reveals that instruct-imagen matches or surpasses prior task-specific models in-domain.
  arXiv  Detail & Related papers  (2024-01-03T19:31:58Z)
• Retrieving Conditions from Reference Images for Diffusion Models [30.14303302029618]
  We consider Subject-Driven Generation as a unified retrieval problem with diffusion models. We introduce a novel diffusion model architecture, named RetriNet, designed to address and solve these problems. We also propose a research and friendly dataset, RetriBooru, to study a more difficult problem, concept composition.
  arXiv  Detail & Related papers  (2023-12-05T06:04:16Z)
• Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation [0.0]
  We introduce Diverse Diffusion, a method for boosting image diversity beyond gender and ethnicity. Our approach contributes to the creation of more inclusive and representative AI-generated art.
  arXiv  Detail & Related papers  (2023-10-19T08:48:23Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Effective Data Augmentation With Diffusion Models [65.09758931804478]
  We address the lack of diversity in data augmentation with image-to-image transformations parameterized by pre-trained text-to-image diffusion models. Our method edits images to change their semantics using an off-the-shelf diffusion model, and generalizes to novel visual concepts from a few labelled examples. We evaluate our approach on few-shot image classification tasks, and on a real-world weed recognition task, and observe an improvement in accuracy in tested domains.
  arXiv  Detail & Related papers  (2023-02-07T20:42:28Z)
• Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images [32.94581354719927]
  We propose a new evaluation metric, called rarity score', to measure the individual rarity of each image. Code will be publicly available online for the research community.
  arXiv  Detail & Related papers  (2022-06-17T05:16:16Z)
• Implicit Data Augmentation Using Feature Interpolation for Diversified Low-Shot Image Generation [11.4559888429977]
  Training of generative models can easily diverge in low-data setting. We propose a novel implicit data augmentation approach which facilitates stable training and synthesize diverse samples.
  arXiv  Detail & Related papers  (2021-12-04T23:55:46Z)

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.