mlsanityData to 14 Oct 2026

Shape complexity estimation using VAE

arXiv 2304.02766 ↗First posted 5 Apr 2023cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with different sizes of latent vectors. Although complexity of a shape is not a well defined attribute, different aspects of it can be estimated. We demonstrate that our methods captures some aspects of shape complexity. Code and training details will be publicly available.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 4 of its 17 references that are in the map. First posted on arXiv 5 Apr 2023.

Builds on

4 of 17 references.

References · 4

  1. 2021Critic Guided Segmentation of Rewarding Objects in First-Person Viewscited by 12
  2. 2022Behavioral Cloning via Search in Video PreTraining Latent Spacecited by 9
  3. 2021Transfer Learning with Jukebox for Music Source Separationcited by 4
  4. 2022Stroke-based Rendering: From Heuristics to Deep Learningcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Benchmarks for Physical Reasoning AIcited by 8
  2. 2025Digital Twin Generation from Visual Data: A Surveycited by 7
  3. 2023Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Taskscited by 5
  4. 2024Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMscited by 4