GANs in Medical Image Synthesis, Translation, and Augmentation - Jason Jeong
Explore GANs in medical imaging for synthetic data generation, addressing scarcity and imbalance issues. Learn about applications in CT, MRI, and disease detection, with insights on challenges and future directions.
Generative Adversarial Minority Oversampling
Explore innovative techniques for addressing class imbalance in machine learning using generative adversarial networks and convex generators, with a focus on minority oversampling.
Multi-Agent Diverse Generative Adversarial Networks
Explore diverse generative adversarial networks, focusing on multi-agent systems, mode collapse prevention, and competing objectives in machine learning.
VEEGAN - Reducing Mode Collapse in GANs Using Implicit Variational Learning
Learn about VEEGAN, an innovative approach to reduce mode collapse in Generative Adversarial Networks (GANs) using implicit variational learning, with practical examples and results.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Explore high-resolution image synthesis and semantic manipulation using conditional GANs, covering advanced techniques like coarse-to-fine generation and multi-scale discriminators for improved photorealism.
Keras Tutorial - Neural Network Architectures and Advanced Concepts - Part 2
Learn advanced deep learning concepts including neural networks, VGG, residual connections, autoencoders, and GANs. Explore batch processing, reproducibility, and practical implementation in Keras.
StyleGAN Paper Explained
Comprehensive explanation of StyleGAN paper, covering architecture, image quality techniques, style properties, mixing, disentanglement, and training details for advanced AI image generation.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI.
Semi-Supervised Learning with GANs in Keras
Explore semi-supervised learning with GANs in Keras, focusing on training discriminators as classifiers using limited labeled data for improved accuracy compared to traditional CNNs.
Semi-Supervised Learning with GANs
Explore semi-supervised learning with GANs, focusing on training discriminators as classifiers using limited labeled data for improved accuracy compared to traditional CNNs.
Single Image Super Resolution Using SRGAN
Explore single image super-resolution using SRGAN, covering key concepts, perceptual loss, content loss, and network architectures for enhanced image quality.
Unpaired Image to Image Translation Using CycleGAN - An Introduction
Introduction to CycleGAN for unpaired image-to-image translation, covering key concepts, architecture, and training process. Explores discriminator and generator components, focusing on PatchGAN and instance normalization.
Image to Image Translation Using Pix2Pix GAN
Learn image-to-image translation using Pix2Pix GAN, exploring key concepts, generator and discriminator architectures, and practical applications in satellite imagery and scientific image generation.
Fixing GAN Optimization Through Competitive Gradient Descent - Anima Anandkumar
Explore competitive gradient descent for optimizing GANs, covering strategic equilibria, applications in ML, and numerical results. Gain insights into modeling competing agents and solving GANs.
Making a 2D GAN 3D Aware
Explore 3D-aware image generation by modifying StyleGANv2 with multiplane alpha maps and pose-conditioned discrimination, achieving high-quality, view-consistent results across diverse datasets.
Exploring the Generative AI Landscape: From Basics to Hands-on Applications
Comprehensive introduction to Generative AI, covering fundamentals, applications, and real-world impact. Includes hands-on demo and discussion of limitations, suitable for beginners and tech enthusiasts.
Image Datasets - StyleGAN - StyleGAN2 - StyleGAN2-ada Focus
Explore ethical image collection, generation, and dataset creation for AI models. Learn about sources, tools, and techniques for building diverse and high-quality image datasets.
Toward Brain Computer Interface - Deep Generative Models for Brain Reading
Explore deep generative models for brain reading, covering GAN implementation, image reconstruction from brain signals, and evolving latent codes using genetic algorithms.
WGAN Implementation From Scratch With Gradient Penalty
Implement WGAN and WGAN-GP in PyTorch, focusing on improving GAN training stability through loss function modifications. Includes theoretical explanations and practical coding.
AudioGen- Textually Guided Audio Generation - Paper Explained
Explore text-guided audio synthesis with AudioGen, delving into its architecture, audio representation, language modeling, and innovative approaches for generating diverse sounds from textual descriptions.
Diffusion Models Beat GANs on Image Synthesis - ML Coding Series - Part 2
Explore diffusion models for image synthesis, focusing on classifier guidance, training, and sampling techniques. Includes code walkthrough and paper analysis.