Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background & Related Work


Method
3.1 Sampling Small Mutations
3.2 Policy
3.3 Value Network & Search
3.4 Architecture


Experiments
4.1 Environments
4.2 Baselines
4.3 Ablations


Conclusion, Acknowledgments and Disclosure of Funding, and References Background & Related Work Background & Related Work Background & Related Work Method
3.1 Sampling Small Mutations
3.2 Policy
3.3 Value Network & Search
3.4 Architecture Method Method 3.1 Sampling Small Mutations 3.1 Sampling Small Mutations 3.2 Policy 3.2 Policy 3.3 Value Network & Search 3.3 Value Network & Search 3.4 Architecture 3.4 Architecture Experiments
4.1 Environments
4.2 Baselines
4.3 Ablations Experiments 4.1 Environments 4.1 Environments 4.2 Baselines 4.2 Baselines 4.3 Ablations 4.3 Ablations Conclusion, Acknowledgments and Disclosure of Funding, and References Conclusion, Acknowledgments and Disclosure of Funding, and References Conclusion, Acknowledgments and Disclosure of Funding, and References Appendix Appendix A. Mutation Algorithm A. Mutation Algorithm B. Context-Free Grammars B. Context-Free Grammars C. Sketch Simulation C. Sketch Simulation D. Complexity Filtering D. Complexity Filtering E. Tree Path Algorithm E. Tree Path Algorithm F. Implementation Details F. Implementation Details D Complexity Filtering As mentioned in Section 4, while testing our method alongside baseline methods, we reached ceiling performance for all our methods. Ellis et al. [11] got around this by creating a “hard” test case by sampling more objects. For us, when we increased the number of objects to increase complexity, we saw that it increased the probability that a large object would be sampled and subtract from the whole scene, resulting in simpler scenes. This is shown by Figure 11(b), which is our training distribution. Even though we sample a large number of objects, the scenes don’t look visually interesting. When we studied the implementation details of Ellis et al. [11], we noticed that during random generation of expressions, they ensured that each shape did not change more that 60% or less than 10% of the pixels in the scene. Instead of modifying our tree sampling method, we instead chose to rejection sample based on the compressibility of the final rendered image. Authors:
(1) Shreyas Kapur, University of California, Berkeley (srkp@cs.berkeley.edu);
(2) Erik Jenner, University of California, Berkeley (jenner@cs.berkeley.edu);
(3) Stuart Russell, University of California, Berkeley (russell@cs.berkeley.edu). Authors: Authors (1) Shreyas Kapur, University of California, Berkeley (srkp@cs.berkeley.edu); (2) Erik Jenner, University of California, Berkeley (jenner@cs.berkeley.edu); (3) Stuart Russell, University of California, Berkeley (russell@cs.berkeley.edu). This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Overcoming Ceiling Performance: Using Complexity Filtering for Harder Inverse Graphics Benchmarks

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Advances in Photocatalytic Materials through SALSA

Experiments with Tree-Diffusion: Testing a New Code Generation Method on Graphics Languages

The Grammar of Code Generation: Detailed CFG Specifications for Graphics Languages

From Program to Sketch: Modeling Non-Deterministic Observations in Code Generation

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

100+ Free Pluralsight Courses to learn Python, Java, and Spring Boot

Advances in Photocatalytic Materials through SALSA

Experiments with Tree-Diffusion: Testing a New Code Generation Method on Graphics Languages

The Grammar of Code Generation: Detailed CFG Specifications for Graphics Languages

From Program to Sketch: Modeling Non-Deterministic Observations in Code Generation

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

100+ Free Pluralsight Courses to learn Python, Java, and Spring Boot

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps