On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2204.05596v1
• Date: Tue, 12 Apr 2022 07:55:47 GMT
• Title: On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
• Authors: Wenju Zhang, Xiang Zhang, Qing Liao, Long Lan, Mengzhu Wang, Wei Wang, Baoyun Peng, Zhengming Ding
• Abstract summary: Nuclear norm has shown the power to enhance the transferability of unsupervised domain adaptation model. Two new losses are proposed to maximize both predictive discriminability and equity, from the class level and the sample level.
• Score: 53.29437277730871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new property termed equity, which indicates the balance degree of predicted classes, to demystify the efficacy of nuclear norm maximization for UDA theoretically. With this in mind, we offer a new discriminability-and-equity maximization paradigm built on squares loss, such that predictions are equalized explicitly. To verify its feasibility and flexibility, two new losses termed Class Weighted Squares Maximization (CWSM) and Normalized Squares Maximization (NSM), are proposed to maximize both predictive discriminability and equity, from the class level and the sample level, respectively. Importantly, we theoretically relate these two novel losses (i.e., CWSM and NSM) to the equity maximization under mild conditions, and empirically suggest the importance of the predictive equity in UDA. Moreover, it is very efficient to realize the equity constraints in both losses. Experiments of cross-domain image classification on three popular benchmark datasets show that both CWSM and NSM contribute to outperforming the corresponding counterparts.

Related papers

• COD: Learning Conditional Invariant Representation for Domain Adaptation Regression [20.676363400841495]
  Domain Adaptation Regression is developed to generalize label knowledge from a source domain to an unlabeled target domain. Existing conditional distribution alignment theory and methods with discrete prior are no longer applicable. To minimize the discrepancy, a COD-based conditional invariant representation learning model is proposed.
  arXiv  Detail & Related papers  (2024-08-13T05:08:13Z)
• Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling [30.835774920236872]
  We study off-policy evaluation of contextual bandit policies for large discrete action spaces. We propose a new estimator, called OffCEM, that is based on the conjunct effect model (CEM), a novel decomposition of the causal effect into a cluster effect and a residual effect. Experiments demonstrate that OffCEM provides substantial improvements in OPE especially in the presence of many actions.
  arXiv  Detail & Related papers  (2023-05-14T04:16:40Z)
• Domain-Specific Risk Minimization for Out-of-Distribution Generalization [104.17683265084757]
  We first establish a generalization bound that explicitly considers the adaptivity gap. We propose effective gap estimation methods for guiding the selection of a better hypothesis for the target. The other method is minimizing the gap directly by adapting model parameters using online target samples.
  arXiv  Detail & Related papers  (2022-08-18T06:42:49Z)
• Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets [101.5329678997916]
  We study episodic two-player zero-sum Markov games (MGs) in the offline setting. The goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori.
  arXiv  Detail & Related papers  (2022-02-15T15:39:30Z)
• Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness [61.827054365139645]
  Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. We propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space.
  arXiv  Detail & Related papers  (2021-10-24T07:58:13Z)
• A Unified Joint Maximum Mean Discrepancy for Domain Adaptation [73.44809425486767]
  This paper theoretically derives a unified form of JMMD that is easy to optimize. From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence that benefits to classification. We propose a novel MMD matrix to promote the dependence, and devise a novel label kernel that is robust to label distribution shift.
  arXiv  Detail & Related papers  (2021-01-25T09:46:14Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization [9.902494567482597]
  In this paper, we consider learning efficient policies that achieve efficiency regarding MV trade-off. To achieve this purpose, we train an agent to maximize the expected quadratic utility function.
  arXiv  Detail & Related papers  (2020-10-03T18:17:34Z)
• Joint Contrastive Learning for Unsupervised Domain Adaptation [20.799729748233343]
  We propose an alternative upper bound on the target error that explicitly considers the joint error to render it more manageable. We introduce Joint Contrastive Learning to find class-level discriminative features, which is essential for minimizing the joint error. Experiments on two real-world datasets demonstrate that JCL outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-18T06:25:34Z)

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.