Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data

• URL: http://arxiv.org/abs/2112.00054v1
• Date: Tue, 30 Nov 2021 19:25:27 GMT
• Title: Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
• Authors: Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu Chen, Leonid Karlinsky, Kate Saenko, Venkatesh Saligrama, Rogerio S. Feris
• Abstract summary: We study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks. We introduce Task2Sim, a unified model mapping downstream task representations to optimal simulation parameters. It learns this mapping by training to find the set of best parameters on a set of "seen" tasks. Once trained, it can then be used to predict best simulation parameters for novel "unseen" tasks in one shot.
• Score: 74.66568380558172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-training models on Imagenet or other massive datasets of real images has led to major advances in computer vision, albeit accompanied with shortcomings related to curation cost, privacy, usage rights, and ethical issues. In this paper, for the first time, we study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks from very different domains. In using such synthetic data for pre-training, we find that downstream performance on different tasks are favored by different configurations of simulation parameters (e.g. lighting, object pose, backgrounds, etc.), and that there is no one-size-fits-all solution. It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance. We introduce Task2Sim, a unified model mapping downstream task representations to optimal simulation parameters to generate synthetic pre-training data for them. Task2Sim learns this mapping by training to find the set of best parameters on a set of "seen" tasks. Once trained, it can then be used to predict best simulation parameters for novel "unseen" tasks in one shot, without requiring additional training. Given a budget in number of images per class, our extensive experiments with 20 diverse downstream tasks show Task2Sim's task-adaptive pre-training data results in significantly better downstream performance than non-adaptively choosing simulation parameters on both seen and unseen tasks. It is even competitive with pre-training on real images from Imagenet.

Related papers

• Scaling Backwards: Minimal Synthetic Pre-training? [52.78699562832907]
  We show that pre-training is effective even with minimal synthetic images. We find that a substantial reduction of synthetic images from 1k to 1 can even lead to an increase in pre-training performance. We extend our method from synthetic images to real images to see if a single real image can show similar pre-training effect.
  arXiv  Detail & Related papers  (2024-08-01T16:20:02Z)
• The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better [39.57368843211441]
  Every synthetic image ultimately originates from the upstream data used to train the generator. We compare finetuning on task-relevant, targeted synthetic data generated by Stable Diffusion against finetuning on targeted real images retrieved directly from LAION-2B. Our analysis suggests that this underperformance is partially due to generator artifacts and inaccurate task-relevant visual details in the synthetic images.
  arXiv  Detail & Related papers  (2024-06-07T18:04:21Z)
• Improving the Effectiveness of Deep Generative Data [5.856292656853396]
  Training a model on purely synthetic images for downstream image processing tasks results in an undesired performance drop compared to training on real data. We propose a new taxonomy to describe factors contributing to this commonly observed phenomenon and investigate it on the popular CIFAR-10 dataset. Our method outperforms baselines on downstream classification tasks both in case of training on synthetic only (Synthetic-to-Real) and training on a mix of real and synthetic data.
  arXiv  Detail & Related papers  (2023-11-07T12:57:58Z)
• SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data [78.21197488065177]
  Recent success in fine-tuning large models, that are pretrained on broad data at scale, on downstream tasks has led to a significant paradigm shift in deep learning. This paper proposes a new task-agnostic framework, textitSynBench, to measure the quality of pretrained representations using synthetic data.
  arXiv  Detail & Related papers  (2022-10-06T15:25:00Z)
• Pro-tuning: Unified Prompt Tuning for Vision Tasks [133.12978197265596]
  Fine-tuning is the de-facto approach to leverage pre-trained vision models to perform downstream tasks. In this work, we propose parameter-efficient Prompt tuning (Pro-tuning) to adapt frozen vision models to various downstream vision tasks.
  arXiv  Detail & Related papers  (2022-07-28T21:09:31Z)
• Replacing Labeled Real-image Datasets with Auto-generated Contours [20.234550996148748]
  We show that formula-driven supervised learning can match or even exceed that of ImageNet-21k without the use of real images. Images generated by formulas avoid the privacy/copyright issues, labeling cost and errors, and biases that real images suffer from.
  arXiv  Detail & Related papers  (2022-06-18T06:43:38Z)
• Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing [76.78772372631623]
  A common practice for self-supervised pre-training is to use as much data as possible. For a specific downstream task, however, involving irrelevant data in pre-training may degenerate the downstream performance. It is burdensome and infeasible to use different downstream-task-customized datasets in pre-training for different tasks.
  arXiv  Detail & Related papers  (2022-05-26T10:49:43Z)
• Two-shot Spatially-varying BRDF and Shape Estimation [89.29020624201708]
  We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. We create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
  arXiv  Detail & Related papers  (2020-04-01T12:56:13Z)

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.