Project Background:
Deep Generative Models (DGMs) represent a cutting-edge area in machine learning and artificial intelligence that aims to replicate and generate complex data distributions. These models have gained prominence for their ability to capture and mimic the underlying patterns of diverse datasets, such as images, text, and audio. One of the main breakthroughs in this field is the development of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which form the cornerstone of many DGMs. GANs employ a competitive training process between a generator and a discriminator, fostering the creation of realistic samples, while VAEs leverage probabilistic models to learn latent representations of data. The synergy of these techniques has led to remarkable advancements in image synthesis, style transfer, and data generation. The application domains of DGMs are extensive, ranging from content creation in the arts to data augmentation for improved model training in various domains.
Problem Statement
- One major concern is the issue of mode collapse in GANs, where the generator fails to explore the entire data distribution and instead focuses on generating a limited set of representative samples.
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This limitation hampers the diversity and quality of the generated content. Another critical problem is the trade-off between the fidelity and diversity of generated samples.
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Striking the right balance between producing realistic and diverse outputs remains challenging.
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Additionally, stability during training is a persistent issue, especially in the context of GANs, where the adversarial training process can be delicate and prone to divergence.
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Moreover, ensuring the interpretability and meaningful disentanglement of latent representations in Variational Autoencoders (VAEs) is an ongoing challenge.
Aim and Objectives
- Develop advanced algorithms for realistic and diverse data generation in DGMs.
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Mitigate mode collapse in GANs.
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Optimize the balance between fidelity and diversity in generated outputs.
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Improve training stability during the learning process.
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Enhance the interpretability of latent representations, especially in VAEs.
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Address ethical concerns related to the potential misuse of generative models.
Contributions to Deep Generative Models
1. Techniques and algorithms have been developed to enhance the stability of training deep generative models, addressing issues like mode collapse and convergence difficulties in models like GANs.
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Methods to promote diversity in generated samples have been introduced, mitigating challenges related to overemphasizing a subset of modes in the data distribution.
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Efforts to improve the interpretability of latent representations in generative models, particularly in VAEs, contribute to a better understanding of the learned features and aid in model analysis.
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Innovations in combining different generative models or incorporating additional structures, such as attention mechanisms, have led to improved performance and the generation of more realistic and diverse content.
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Considerations for making deep generative models more energy-efficient have emerged, addressing environmental concerns associated with the computational demands of training large-scale models.
Deep Learning Algorithms for Deep Generative Models
- Generative Adversarial Networks (GANs)
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Variational Autoencoders (VAEs)
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Boltzmann Machines
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Restricted Boltzmann Machines (RBMs)
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Adversarially Regularized Autoencoders (ARAE)
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InfoGAN (Information Maximizing Generative Adversarial Networks)
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Conditional Variational Autoencoders (CVAEs)
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Energy-Based Models (EBMs)
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Neural Autoregressive Distribution Estimation (NADE)
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Wasserstein GAN (WGAN)
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Deep Convolutional Generative Adversarial Networks (DCGAN)
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Self-Attention Generative Adversarial Networks (SAGAN)
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CycleGAN (Cycle-Consistent Adversarial Networks)
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Stacked Generative Adversarial Networks (StackGAN)
Datasets for Deep Generative Models
- CelebA
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MNIST
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Fashion-MNIST
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ImageNet
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LFW (Labeled Faces in the Wild)
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Cityscapes
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Omniglot
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ADE20K (ADE20K Semantic Segmentation Dataset)
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Pascal VOC
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Caltech-UCSD Birds-200-2011
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KITTI Vision Benchmark Suite
Performance Metrics
- Inception Score (IS)
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Frechet Inception Distance (FID)
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Precision and Recall
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Kernel Inception Distance (KID)
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Structural Similarity Index (SSI)
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Perceptual Path Length (PPL)
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Diversity and Coverage Metrics
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SWD (Sliced Wasserstein Distance)
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Negative Log-Likelihood (NLL)
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Parzen Window Density Estimation
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Kullback-Leibler (KL) Divergence
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Wasserstein Distance
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Mean Squared Error (MSE)
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Jensen-Shannon Divergence
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
