filmov
tv
StackGAN Implementation| Text to Image Generation with Stacked Generative Adversarial Networks
Показать описание
Tensorflow implementation of StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
“Generative Adversarial Networks (GAN) is the most interesting idea in the last ten years in machine learning.” — Yann LeCun, Director, Facebook AI
Implementation:
StackGAN: Text to Photo-Realistic Image Synthesis
Model Architecture of StackGAN
Preparation of Dataset
Implementation of Stage I of StackGAN
Implementation of Stage II of StackGAN
At the end of this video, you will have a working model of StackGAN research paper, generating photorealistic images from text. Moreover, you will have a knowledge of how to train StackGAN for your own dataset or problem statement.
Example:
(Text Input): The bird is black with green and has a very short beak
(Output — Generated photo-realistic images)
The model architecture of StackGAN consists of mainly the following components:
Embedding: Converts the input variable length text into a fixed length vector. we will be using a pre-trained character level embedding.
Conditioning Augmentation (CA)
Stage I Generator: Generates low resolution (64*64) images.
Stage I Discriminator
Residual Blocks
Stage II Generator: Generates high resolution (256*256) images.
Stage II Discriminator
#stackgan #gans #generativeadversarialnetworks #neuralnetworks #ai #deeplearning #computervision #deeplearning #ml #machinelearning #pifordtechnologies #generativeai #generativemodels
“Generative Adversarial Networks (GAN) is the most interesting idea in the last ten years in machine learning.” — Yann LeCun, Director, Facebook AI
Implementation:
StackGAN: Text to Photo-Realistic Image Synthesis
Model Architecture of StackGAN
Preparation of Dataset
Implementation of Stage I of StackGAN
Implementation of Stage II of StackGAN
At the end of this video, you will have a working model of StackGAN research paper, generating photorealistic images from text. Moreover, you will have a knowledge of how to train StackGAN for your own dataset or problem statement.
Example:
(Text Input): The bird is black with green and has a very short beak
(Output — Generated photo-realistic images)
The model architecture of StackGAN consists of mainly the following components:
Embedding: Converts the input variable length text into a fixed length vector. we will be using a pre-trained character level embedding.
Conditioning Augmentation (CA)
Stage I Generator: Generates low resolution (64*64) images.
Stage I Discriminator
Residual Blocks
Stage II Generator: Generates high resolution (256*256) images.
Stage II Discriminator
#stackgan #gans #generativeadversarialnetworks #neuralnetworks #ai #deeplearning #computervision #deeplearning #ml #machinelearning #pifordtechnologies #generativeai #generativemodels
1:10:53
StackGAN Implementation| Text to Image Generation with Stacked Generative Adversarial Networks
0:18:20
StackGAN | Text to Image Generation with Stacked Generative Adversarial Networks
0:04:34
StackGAN
0:09:33
How to Convert Text to Images - Intro to Deep Learning #16
0:34:28
#9 StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
0:06:57
Learning Generative Adversarial Networks: Introduction to StackGAN| packtpub.com
0:05:40
TEXT TO IMAGE USING GAN (GENERATIVE ADVERSARIAL NETWORKS)
0:04:06
Image Synthesis From Text With Deep Learning | Two Minute Papers #116
2:21:14
Image Generation using GANs | Deep Learning with PyTorch (6/6)
0:18:03
StackGAN: Text to Photorealistic Image Synthesis using StackGAN [PAPER EXPLAINED]
0:08:14
Text To Image Generator | Machine Learning Project | NLP Project | Hugging Face | Stable Diffusion
0:17:42
Text to Image AI Models: Different methodologies and different models, how it works?
0:11:00
StackGAN | Lecture 61 (Part 2) | Applied Deep Learning (Supplementary)
2:01:24
Build a Generative Adversarial Neural Network with Tensorflow and Python | Deep Learning Projects
0:17:24
SRGAN Implementation on Custom dataset | Super Resolution GAN
0:19:49
12 Must read Text to Image AI Research Papers with their code implementation
0:00:58
New AI text-to-image generator - Google Muse #AI model
0:11:54
Text to Image Generation using Tensorflow
2:29:15
Image Generation using GANs | Deep Learning with PyTorch: Zero to GANs | Part 6 of 6
0:44:36
MIT 6.S191 (2023): Text-to-Image Generation
0:01:24
Generating Text Based Images - Part 1
1:48:54
Muse: Text to Image with Transformers
0:08:57
Text to Image using Deep Learning
0:10:12
Text to Image Synthesis (Continued) | Lecture 61 (Part 1) | Applied Deep Learning (Supplementary)
Комментарии