What are some examples and challenges of generative modeling?
However, generative modeling also faces many challenges and limitations, such as:
Challenge | Description |
|---|---|
Training complexity | Generative models require significant computational resources and time. |
Quality control | While they can produce vast amounts of data, ensuring the quality and realism of the generated content can be challenging. |
Overfitting | Generative models may learn the data too well and produce outputs that are too similar to the original data or fail to generalize to new data. |
Lack of interpretability | Generative models may not provide a clear explanation of how they generate the data, or what features or patterns they learn from the data. |
Ethical concerns | Generative models may generate data that are harmful, misleading, or malicious, such as fake news, deepfakes, spam, or phishing. |
Data dependency | Generative models may depend on the availability and quality of the data, and may not perform well on data that are scarce, imbalanced, or noisy. |
Mode collapse | Generative models may produce only a few modes of the data distribution, and ignore the rest, resulting in a lack of diversity and variety in the outputs. |
Generative Modeling in TensorFlow
Generative modeling is the process of learning the underlying structure of a dataset to generate new samples that mimic the distribution of the original data. The article aims to provide a comprehensive overview of generative modelling along with the implementation leveraging the TensorFlow framework.
Table of Content
- What are generative models and how do they work?
- What are the main types of generative models and how are they different?
- How to use TensorFlow to build and train generative models?
- Implementation Steps
- How to evaluate and compare generative models ?
- What are some examples and challenges of generative modeling ?
- Example 1: Basic autoencoder
- Example 2: Image denoising