Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency

• URL: http://arxiv.org/abs/2003.06635v1
• Date: Sat, 14 Mar 2020 14:06:46 GMT
• Title: Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
• Authors: Guansong Lu, Zhiming Zhou, Jian Shen, Cheng Chen, Weinan Zhang, Yong Yu
• Abstract summary: We propose an end-to-end approach for large-scale optimal transport, which directly solves the transport map and is compatible with general cost function. We demonstrate the effectiveness of the proposed method against existing methods with large-scale real-world applications.
• Score: 30.305690062622283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large-scale optimal transport have greatly extended its application scenarios in machine learning. However, existing methods either not explicitly learn the transport map or do not support general cost function. In this paper, we propose an end-to-end approach for large-scale optimal transport, which directly solves the transport map and is compatible with general cost function. It models the transport map via stochastic neural networks and enforces the constraint on the marginal distributions via adversarial training. The proposed framework can be further extended towards learning Monge map or optimal bijection via adopting cycle-consistency constraint(s). We verify the effectiveness of the proposed method and demonstrate its superior performance against existing methods with large-scale real-world applications, including domain adaptation, image-to-image translation, and color transfer.

Related papers

• Improving Neural Optimal Transport via Displacement Interpolation [16.474572112062535]
  Optimal Transport (OT) theory investigates the cost-minimizing transport map that moves a source distribution to a target distribution. We propose a novel method to improve stability and achieve a better approximation of the OT Map by exploiting displacement. We demonstrate that DIOTM outperforms existing OT-based models on image-to-image translation tasks.
  arXiv  Detail & Related papers  (2024-10-03T16:42:23Z)
• FlowIE: Efficient Image Enhancement via Rectified Flow [71.6345505427213]
  FlowIE is a flow-based framework that estimates straight-line paths from an elementary distribution to high-quality images. Our contributions are rigorously validated through comprehensive experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-06-01T17:29:29Z)
• Control, Transport and Sampling: Towards Better Loss Design [10.732151772173083]
  We propose objective functions that can be used to transport $nu$ to $mu$, via optimally controlled dynamics. We highlight the importance of the pathwise perspective and the role various optimality conditions on the path measure can play for the design of valid training losses.
  arXiv  Detail & Related papers  (2024-05-22T15:24:48Z)
• InfoOT: Information Maximizing Optimal Transport [58.72713603244467]
  InfoOT is an information-theoretic extension of optimal transport. It maximizes the mutual information between domains while minimizing geometric distances. This formulation yields a new projection method that is robust to outliers and generalizes to unseen samples.
  arXiv  Detail & Related papers  (2022-10-06T18:55:41Z)
• Neural Optimal Transport with General Cost Functionals [66.41953045707172]
  We introduce a novel neural network-based algorithm to compute optimal transport plans for general cost functionals. As an application, we construct a cost functional to map data distributions while preserving the class-wise structure.
  arXiv  Detail & Related papers  (2022-05-30T20:00:19Z)
• Neural Optimal Transport [82.2689844201373]
  We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. We prove that neural networks are universal approximators of transport plans between probability distributions.
  arXiv  Detail & Related papers  (2022-01-28T16:24:13Z)
• A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
  We propose a deep reinforcement learning based solution for order dispatching. We conduct large scale online A/B tests on DiDi's ride-dispatching platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods.
  arXiv  Detail & Related papers  (2021-06-08T16:27:04Z)
• Scalable Bayesian Inverse Reinforcement Learning [93.27920030279586]
  We introduce Approximate Variational Reward Imitation Learning (AVRIL) Our method addresses the ill-posed nature of the inverse reinforcement learning problem. Applying our method to real medical data alongside classic control simulations, we demonstrate Bayesian reward inference in environments beyond the scope of current methods.
  arXiv  Detail & Related papers  (2021-02-12T12:32:02Z)
• Bayesian Inference for Optimal Transport with Stochastic Cost [22.600086666266243]
  In machine learning and computer vision, optimal transport has had significant success in learning generative models. We introduce a framework for inferring the optimal transport plan distribution by induced cost. We also tailor an HMC method to sample from the resulting transport plan distribution.
  arXiv  Detail & Related papers  (2020-10-19T09:07:57Z)
• Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle [0.0]
  This paper proposes an improved method to achieve path planning for UAVs in complex surroundings: multiple no-fly zones. The results show the feasibility and efficiency of the model applying in this kind of complicated situation.
  arXiv  Detail & Related papers  (2020-04-21T09:42:03Z)

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.