Evaluating the Stability of Deep Learning Latent Feature Spaces
- URL: http://arxiv.org/abs/2402.11404v2
- Date: Fri, 23 Feb 2024 20:59:54 GMT
- Title: Evaluating the Stability of Deep Learning Latent Feature Spaces
- Authors: Ademide O. Mabadeje and Michael J. Pyrcz
- Abstract summary: This study introduces a novel workflow to evaluate the stability of latent spaces, ensuring consistency and reliability in subsequent analyses.
We implement this workflow across 500 autoencoder realizations and three datasets, encompassing both synthetic and real-world scenarios.
Our findings highlight inherent instabilities in latent feature spaces and demonstrate the workflow's efficacy in quantifying and interpreting these instabilities.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-dimensional datasets present substantial challenges in statistical
modeling across various disciplines, necessitating effective dimensionality
reduction methods. Deep learning approaches, notable for their capacity to
distill essential features from complex data, facilitate modeling,
visualization, and compression through reduced dimensionality latent feature
spaces, have wide applications from bioinformatics to earth sciences. This
study introduces a novel workflow to evaluate the stability of these latent
spaces, ensuring consistency and reliability in subsequent analyses. Stability,
defined as the invariance of latent spaces to minor data, training
realizations, and parameter perturbations, is crucial yet often overlooked.
Our proposed methodology delineates three stability types, sample,
structural, and inferential, within latent spaces, and introduces a suite of
metrics for comprehensive evaluation. We implement this workflow across 500
autoencoder realizations and three datasets, encompassing both synthetic and
real-world scenarios to explain latent space dynamics. Employing k-means
clustering and the modified Jonker-Volgenant algorithm for class alignment,
alongside anisotropy metrics and convex hull analysis, we introduce adjusted
stress and Jaccard dissimilarity as novel stability indicators.
Our findings highlight inherent instabilities in latent feature spaces and
demonstrate the workflow's efficacy in quantifying and interpreting these
instabilities. This work advances the understanding of latent feature spaces,
promoting improved model interpretability and quality control for more informed
decision-making for diverse analytical workflows that leverage deep learning.
Related papers
- Stability Evaluation via Distributional Perturbation Analysis [28.379994938809133]
We propose a stability evaluation criterion based on distributional perturbations.
Our stability evaluation criterion can address both emphdata corruptions and emphsub-population shifts.
Empirically, we validate the practical utility of our stability evaluation criterion across a host of real-world applications.
arXiv Detail & Related papers (2024-05-06T06:47:14Z) - Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning [33.560003528712414]
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data.
This poses a challenge in striking a balance between stability and plasticity when adapting to new information.
We propose Branch-tuning, an efficient and straightforward method that achieves a balance between stability and plasticity in continual SSL.
arXiv Detail & Related papers (2024-03-27T05:38:48Z) - The Risk of Federated Learning to Skew Fine-Tuning Features and
Underperform Out-of-Distribution Robustness [50.52507648690234]
Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model.
We introduce three robustness indicators and conduct experiments across diverse robust datasets.
Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
arXiv Detail & Related papers (2024-01-25T09:18:51Z) - Distributionally Robust Model-based Reinforcement Learning with Large
State Spaces [55.14361269378122]
Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment.
We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets.
We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
arXiv Detail & Related papers (2023-09-05T13:42:11Z) - New metrics for analyzing continual learners [27.868967961503962]
Continual Learning (CL) poses challenges to standard learning algorithms.
This stability-plasticity dilemma remains central to CL and multiple metrics have been proposed to adequately measure stability and plasticity separately.
We propose new metrics that account for the task's increasing difficulty.
arXiv Detail & Related papers (2023-09-01T13:53:33Z) - Learning World Models with Identifiable Factorization [39.767120163665574]
We propose IFactor to model four distinct categories of latent state variables.
Our analysis establishes block-wise identifiability of these latent variables.
We present a practical approach to learning the world model with identifiable blocks.
arXiv Detail & Related papers (2023-06-11T02:25:15Z) - Information-Theoretic Odometry Learning [83.36195426897768]
We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation.
The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
arXiv Detail & Related papers (2022-03-11T02:37:35Z) - Learning Conditional Invariance through Cycle Consistency [60.85059977904014]
We propose a novel approach to identify meaningful and independent factors of variation in a dataset.
Our method involves two separate latent subspaces for the target property and the remaining input information.
We demonstrate on synthetic and molecular data that our approach identifies more meaningful factors which lead to sparser and more interpretable models.
arXiv Detail & Related papers (2021-11-25T17:33:12Z) - Learning while Respecting Privacy and Robustness to Distributional
Uncertainties and Adversarial Data [66.78671826743884]
The distributionally robust optimization framework is considered for training a parametric model.
The objective is to endow the trained model with robustness against adversarially manipulated input data.
Proposed algorithms offer robustness with little overhead.
arXiv Detail & Related papers (2020-07-07T18:25:25Z) - Dynamic Federated Learning [57.14673504239551]
Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments.
We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data.
Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm.
arXiv Detail & Related papers (2020-02-20T15:00:54Z)
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.