Unifying Self-Supervised Clustering and Energy-Based Models
- URL: http://arxiv.org/abs/2401.00873v3
- Date: Thu, 28 Nov 2024 19:34:27 GMT
- Title: Unifying Self-Supervised Clustering and Energy-Based Models
- Authors: Emanuele Sansone, Robin Manhaeve,
- Abstract summary: We establish a principled connection between self-supervised learning and generative models.
We show that our solution can be integrated into a neuro-symbolic framework to tackle a simple yet non-trivial instantiation of the symbol grounding problem.
- Score: 9.3176264568834
- License:
- Abstract: Self-supervised learning excels at learning representations from large amounts of data. At the same time, generative models offer the complementary property of learning information about the underlying data generation process. In this study, we aim at establishing a principled connection between these two paradigms and highlight the benefits of their complementarity. In particular, we perform an analysis of self-supervised learning objectives, elucidating the underlying probabilistic graphical models and presenting a standardized methodology for their derivation from first principles. The analysis suggests a natural means of integrating self-supervised learning with likelihood-based generative models. We instantiate this concept within the realm of cluster-based self-supervised learning and energy models, introducing a lower bound proven to reliably penalize the most important failure modes. Our theoretical findings are substantiated through experiments on synthetic and real-world data, including SVHN, CIFAR10, and CIFAR100, demonstrating that our objective function allows to jointly train a backbone network in a discriminative and generative fashion, consequently outperforming existing self-supervised learning strategies in terms of clustering, generation and out-of-distribution detection performance by a wide margin. We also demonstrate that the solution can be integrated into a neuro-symbolic framework to tackle a simple yet non-trivial instantiation of the symbol grounding problem.
Related papers
- Exploring the Precise Dynamics of Single-Layer GAN Models: Leveraging Multi-Feature Discriminators for High-Dimensional Subspace Learning [0.0]
We study the training dynamics of a single-layer GAN model from the perspective of subspace learning.
By bridging our analysis to the realm of subspace learning, we systematically compare the efficacy of GAN-based methods against conventional approaches.
arXiv Detail & Related papers (2024-11-01T10:21:12Z) - Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition [13.593511876719367]
We propose a novel skeleton-based idempotent generative model (IGM) for unsupervised representation learning.
Our experiments on benchmark datasets, NTU RGB+D and PKUMMD, demonstrate the effectiveness of our proposed method.
arXiv Detail & Related papers (2024-10-27T06:29:04Z) - A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels.
We present a generative latent variable model for self-supervised learning.
We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
arXiv Detail & Related papers (2024-02-02T13:31:17Z) - A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
We propose a general bi-level probabilistic graphical reasoning framework called GBPGR.
In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models.
Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
arXiv Detail & Related papers (2023-09-16T09:15:37Z) - Semi-supervised learning made simple with self-supervised clustering [65.98152950607707]
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations.
We propose a conceptually simple yet empirically powerful approach to turn clustering-based self-supervised methods into semi-supervised learners.
arXiv Detail & Related papers (2023-06-13T01:09:18Z) - GEDI: GEnerative and DIscriminative Training for Self-Supervised
Learning [3.6804038214708563]
We study state-of-the-art self-supervised learning objectives and propose a unified formulation based on likelihood learning.
We refer to this combined framework as GEDI, which stands for GEnerative and DIscriminative training.
We show that GEDI outperforms existing self-supervised learning strategies in terms of clustering performance by a wide margin.
arXiv Detail & Related papers (2022-12-27T09:33:50Z) - Bridging the Gap to Real-World Object-Centric Learning [66.55867830853803]
We show that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way.
Our approach, DINOSAUR, significantly out-performs existing object-centric learning models on simulated data.
arXiv Detail & Related papers (2022-09-29T15:24:47Z) - A Unified Contrastive Energy-based Model for Understanding the
Generative Ability of Adversarial Training [64.71254710803368]
Adversarial Training (AT) is an effective approach to enhance the robustness of deep neural networks.
We demystify this phenomenon by developing a unified probabilistic framework, called Contrastive Energy-based Models (CEM)
We propose a principled method to develop adversarial learning and sampling methods.
arXiv Detail & Related papers (2022-03-25T05:33:34Z) - Self-organizing Democratized Learning: Towards Large-scale Distributed
Learning Systems [71.14339738190202]
democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems.
Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper.
The proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms.
arXiv Detail & Related papers (2020-07-07T08:34:48Z)
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.