Learning Hierarchical Features with Joint Latent Space Energy-Based Prior

• URL: http://arxiv.org/abs/2310.09604v1
• Date: Sat, 14 Oct 2023 15:44:14 GMT
• Title: Learning Hierarchical Features with Joint Latent Space Energy-Based Prior
• Authors: Jiali Cui, Ying Nian Wu, Tian Han
• Abstract summary: We study the fundamental problem of multi-layer generator models in learning hierarchical representations. We propose a joint latent space EBM prior model with multi-layer latent variables for effective hierarchical representation learning.
• Score: 44.4434704520236
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper studies the fundamental problem of multi-layer generator models in learning hierarchical representations. The multi-layer generator model that consists of multiple layers of latent variables organized in a top-down architecture tends to learn multiple levels of data abstraction. However, such multi-layer latent variables are typically parameterized to be Gaussian, which can be less informative in capturing complex abstractions, resulting in limited success in hierarchical representation learning. On the other hand, the energy-based (EBM) prior is known to be expressive in capturing the data regularities, but it often lacks the hierarchical structure to capture different levels of hierarchical representations. In this paper, we propose a joint latent space EBM prior model with multi-layer latent variables for effective hierarchical representation learning. We develop a variational joint learning scheme that seamlessly integrates an inference model for efficient inference. Our experiments demonstrate that the proposed joint EBM prior is effective and expressive in capturing hierarchical representations and modelling data distribution.

Related papers

• COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models [14.130327598928778]
  Large language models (LLMs) and hierarchical motion-specific vector-quantized variational autoencoders (VQ-VAEs) are proposed. Our framework generates realistic and diverse collaborative human-object-human interactions, outperforming state-of-the-art methods. Our work opens up new possibilities for modeling complex interactions in various domains, such as robotics, graphics and computer vision.
  arXiv  Detail & Related papers  (2024-09-30T17:02:13Z)
• Learning Joint Latent Space EBM Prior Model for Multi-layer Generator [44.4434704520236]
  We study the fundamental problem of learning multi-layer generator models. We propose an energy-based model (EBM) on the joint latent space over all layers of latent variables. Our experiments demonstrate that the learned model can be expressive in generating high-quality images.
  arXiv  Detail & Related papers  (2023-06-10T00:27:37Z)
• Dynamic Latent Separation for Deep Learning [67.62190501599176]
  A core problem in machine learning is to learn expressive latent variables for model prediction on complex data. Here, we develop an approach that improves expressiveness, provides partial interpretation, and is not restricted to specific applications.
  arXiv  Detail & Related papers  (2022-10-07T17:56:53Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)
• Recurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing [9.645196221785694]
  We develop an approach called the latent indicator layer to identify and learn implicit hierarchical information. We also develop an EM algorithm to handle the latent indicator layer in training. We show that the EM-HRNN model with bootstrap training outperforms other RNN-based models in document classification tasks.
  arXiv  Detail & Related papers  (2022-01-21T23:08:33Z)
• Hierarchical Variational Memory for Few-shot Learning Across Domains [120.87679627651153]
  We introduce a hierarchical prototype model, where each level of the prototype fetches corresponding information from the hierarchical memory. The model is endowed with the ability to flexibly rely on features at different semantic levels if the domain shift circumstances so demand. We conduct thorough ablation studies to demonstrate the effectiveness of each component in our model.
  arXiv  Detail & Related papers  (2021-12-15T15:01:29Z)
• HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression [53.90578309960526]
  Large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. We propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information.
  arXiv  Detail & Related papers  (2021-10-16T11:23:02Z)

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.