Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints

• URL: http://arxiv.org/abs/2011.12659v1
• Date: Wed, 25 Nov 2020 11:40:10 GMT
• Title: Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
• Authors: Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
• Abstract summary: Constr-DRKM is a deep kernel method for the unsupervised learning of disentangled data representations. We quantitatively evaluate the proposed method's effectiveness in disentangled feature learning.
• Score: 15.296955630621566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine formulation for kernel PCA by orthogonality constraints on the latent variables to promote disentanglement and to make it possible to carry out optimization without first defining a stabilized objective. After illustrating an end-to-end training procedure based on a quadratic penalty optimization algorithm with warm start, we quantitatively evaluate the proposed method's effectiveness in disentangled feature learning. We demonstrate on four benchmark datasets that this approach performs similarly overall to $\beta$-VAE on a number of disentanglement metrics when few training points are available, while being less sensitive to randomness and hyperparameter selection than $\beta$-VAE. We also present a deterministic initialization of Constr-DRKM's training algorithm that significantly improves the reproducibility of the results. Finally, we empirically evaluate and discuss the role of the number of layers in the proposed methodology, examining the influence of each principal component in every layer and showing that components in lower layers act as local feature detectors capturing the broad trends of the data distribution, while components in deeper layers use the representation learned by previous layers and more accurately reproduce higher-level features.

Related papers

• Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric [99.19559537966538]
  DML aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-03T13:44:20Z)
• Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning [53.445068584013896]
  We study matrix estimation problems arising in reinforcement learning (RL) with low-rank structure. In low-rank bandits, the matrix to be recovered specifies the expected arm rewards, and for low-rank Markov Decision Processes (MDPs), it may for example characterize the transition kernel of the MDP. We show that simple spectral-based matrix estimation approaches efficiently recover the singular subspaces of the matrix and exhibit nearly-minimal entry-wise error.
  arXiv  Detail & Related papers  (2023-10-10T17:06:41Z)
• Provably Efficient UCB-type Algorithms For Learning Predictive State Representations [55.00359893021461]
  The sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs) This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
  arXiv  Detail & Related papers  (2023-07-01T18:35:21Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Entropic Wasserstein Component Analysis [8.744017403796406]
  A key requirement for Dimension reduction (DR) is to incorporate global dependencies among original and embedded samples. We combine the principles of optimal transport (OT) and principal component analysis (PCA) Our method seeks the best linear subspace that minimizes reconstruction error using entropic OT, which naturally encodes the neighborhood information of the samples.
  arXiv  Detail & Related papers  (2023-03-09T08:59:33Z)
• GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond [101.5329678997916]
  We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making. We propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR.
  arXiv  Detail & Related papers  (2022-11-03T16:42:40Z)
• SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform [0.0]
  We propose a Spectral data Classification framework with Adaptive Inference. Specifically, to allocate different computations for different samples while better exploiting the collaboration among different devices. To the best of our knowledge, this paper is the first attempt to conduct optimization by adaptive inference for spectral detection under the IoT platform.
  arXiv  Detail & Related papers  (2022-06-24T09:22:52Z)
• Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference [1.066048003460524]
  Post-earthquake regional damage assessment of buildings is an expensive task. The information theoretic measure of mutual information is one of the most effective criteria to evaluate the effectiveness of the samples. A local kernels strategy was proposed to reduce the computational costs, but the adaptability of the kernels to the observed labels was not considered. In this article, an adaptive local kernels methodology is developed that allows for the conformability of the kernels to the observed output data.
  arXiv  Detail & Related papers  (2021-05-24T18:34:46Z)
• Feature space approximation for kernel-based supervised learning [2.653409741248232]
  The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. We demonstrate significant improvements in comparison to the computation of data-driven predictions involving the full training data set. The method is applied to classification and regression problems from different application areas such as image recognition, system identification, and oceanographic time series analysis.
  arXiv  Detail & Related papers  (2020-11-25T11:23:58Z)
• Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation [54.49894381464853]
  We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
  arXiv  Detail & Related papers  (2020-07-21T13:27:09Z)
• Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows [0.0]
  This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems. It combines a novel, "degenerate" version of the prototypical policy optimization (PPO) algorithm, that trains a neural network in optimizing the system only once per learning episode.
  arXiv  Detail & Related papers  (2020-06-04T16:11:26Z)

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.