Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation

• URL: http://arxiv.org/abs/2107.06154v1
• Date: Tue, 13 Jul 2021 15:08:32 GMT
• Title: Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation
• Authors: Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang and Qi Tian
• Abstract summary: We study the prediction discriminability and diversity by studying the structure of the classification output matrix of a randomly selected data batch. We propose Batch Nuclear-norm Maximization and Minimization, which performs nuclear-norm on the target output matrix to enhance the target prediction ability. Experiments show that our method could boost the adaptation accuracy and robustness under three typical domain adaptation scenarios.
• Score: 154.2195491708548
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain. A common solution is to minimize the Shannon Entropy to push the decision boundary away from the high density area. However, entropy minimization also leads to severe reduction of prediction diversity, and unfortunately brings harm to the domain adaptation. In this paper, we investigate the prediction discriminability and diversity by studying the structure of the classification output matrix of a randomly selected data batch. We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix. The nuclear-norm is an upperbound of the former, and a convex approximation of the latter. Accordingly, we propose Batch Nuclear-norm Maximization and Minimization, which performs nuclear-norm maximization on the target output matrix to enhance the target prediction ability, and nuclear-norm minimization on the source batch output matrix to increase applicability of the source domain knowledge. We further approximate the nuclear-norm by L_{1,2}-norm, and design multi-batch optimization for stable solution on large number of categories. The fast approximation method achieves O(n^2) computational complexity and better convergence property. Experiments show that our method could boost the adaptation accuracy and robustness under three typical domain adaptation scenarios. The code is available at https://github.com/cuishuhao/BNM.

Related papers

• Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature [0.0]
  Gauss-Hermite quadrature is an accurate alternative to Monte Carlo for both independent and correlated predictive densities. We show it can be an accurate alternative to Monte Carlo for both independent and correlated predictive densities.
  arXiv  Detail & Related papers  (2022-06-15T22:09:48Z)
• A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence [108.28566246421742]
  This paper develops a dimensionality reduction method that allows us to move the optimization to a finite-dimensional setting with an explicit bound on the dimension. In order to make progress on the problem, we restrict ourselves to Bayesian risks induced by a relatively large class of loss functions, namely Bregman divergences.
  arXiv  Detail & Related papers  (2022-02-23T16:22:28Z)
• Optimal policy evaluation using kernel-based temporal difference methods [78.83926562536791]
  We use kernel Hilbert spaces for estimating the value function of an infinite-horizon discounted Markov reward process. We derive a non-asymptotic upper bound on the error with explicit dependence on the eigenvalues of the associated kernel operator. We prove minimax lower bounds over sub-classes of MRPs.
  arXiv  Detail & Related papers  (2021-09-24T14:48:20Z)
• Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds [37.03247707259297]
  A suitable adaptive subspace can be generated by sampling a correlated random matrix whose second order statistics mirror the input data. We show that the relative error of the randomized approximations can be tightly characterized in terms of the spectrum of the data matrix. Experimental results show that the proposed approach enables significant speed ups in a wide variety of machine learning and optimization problems.
  arXiv  Detail & Related papers  (2020-12-13T13:02:31Z)
• Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation [99.92568326314667]
  We propose the amortized conditional normalized maximum likelihood (ACNML) method as a scalable general-purpose approach for uncertainty estimation. Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle. We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of calibration on out-of-distribution inputs.
  arXiv  Detail & Related papers  (2020-11-05T08:04:34Z)
• Large-Scale Methods for Distributionally Robust Optimization [53.98643772533416]
  We prove that our algorithms require a number of evaluations gradient independent of training set size and number of parameters. Experiments on MNIST and ImageNet confirm the theoretical scaling of our algorithms, which are 9--36 times more efficient than full-batch methods.
  arXiv  Detail & Related papers  (2020-10-12T17:41:44Z)
• Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions [41.7567932118769]
  Empirical Risk Minimization algorithms are widely used in a variety of estimation and prediction tasks. In this paper, we characterize for the first time the fundamental limits on the statistical accuracy of convex ERM for inference.
  arXiv  Detail & Related papers  (2020-06-16T04:27:38Z)
• Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations [154.51144248210338]
  Batch Nuclear-norm Maximization (BNM) is proposed to boost the learning under label insufficient learning scenarios. BNM outperforms competitors and works well with existing well-known methods.
  arXiv  Detail & Related papers  (2020-03-27T05:04:24Z)

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.