ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2106.03620
  4. Cited By
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network
  For Inverse Design

PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

7 June 2021
Amin Heyrani Nobari
Wei Chen
Faez Ahmed
ArXivPDFHTML

Papers citing "PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design"

7 / 7 papers shown
Title
Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network
Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network
Debasmita Pal
Arun Ross
GAN
116
1
0
17 Jan 2025
Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
Phillip Mueller
Lars Mikelsons
AI4CE
74
2
0
15 Jul 2024
CCDM: Continuous Conditional Diffusion Models for Image Generation
CCDM: Continuous Conditional Diffusion Models for Image Generation
Xin Ding
Member Ieee Yongwei Wang
Kao Zhang
F. I. Z. Jane Wang
DiffM
69
4
0
06 May 2024
Generative Design by Reinforcement Learning: Enhancing the Diversity of
  Topology Optimization Designs
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs
Seowoo Jang
Soyoung Yoo
Namwoo Kang
AI4CE
56
71
0
17 Aug 2020
Modulating early visual processing by language
Modulating early visual processing by language
H. D. Vries
Florian Strub
Jérémie Mary
Hugo Larochelle
Olivier Pietquin
Aaron Courville
103
484
0
02 Jul 2017
Objective-Reinforced Generative Adversarial Networks (ORGAN) for
  Sequence Generation Models
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
G. L. Guimaraes
Benjamín Sánchez-Lengeling
Carlos Outeiral
Pedro Luis Cunha Farias
Alán Aspuru-Guzik
GAN
69
523
0
30 May 2017
Determinantal point processes for machine learning
Determinantal point processes for machine learning
Alex Kulesza
B. Taskar
220
1,130
0
25 Jul 2012
1