Generative AI models are behind the revolution that many fields are enjoying. It has done so by enabling the creation of original and new content. Some of the most popular and widely used generative AI models are-
GANs were invented by Ian Goodfellow in 2014. These include two neural networks. The first is the generator and second is the discriminator. Both of these are pitted against one another. The former creates data, while the latter evaluates it. This leads to significantly enhanced outputs.
- Video synthesis
- Image generation (e.g., DeepArt, StyleGAN)
- Data augmentation
Check out our Generative AI certification training program to get in-depth knowledge of Gen AI.
VAEs were invented by Kingma & Welling in 2013. These encode input data inside a latent space. Once there, it is decoded back. The purpose is to introduce some noise for the creation of variability in the outputs.
Transformer-based models were invented by Vaswani et al. in 2017. Transformers employ self-attention mechanisms for processing input data in parallel. This renders them exceptionally beneficial for sequence-to-sequence tasks.
DALL-E2 was invented by OpenAI in 2021. It is a popular model that fluently generates images from the textual descriptions entered by the users. It employs a blend of diffusion techniques and transformer models.
Applications
Pros
Cons
Both of these were invented by Hochreiter and Schmidhuber in 1997. LSTMs and RNNs are crafted to work with sequential data. This is done by maintaining a core memory of previous inputs. This makes them apt for language data and time-series.
Related Article- Introduction To Generative AI Tools
Generative AI models comprise plenty of technologies crafted to create new content. This content could be text, images, music, or any other forms of media. Some of the most prominent types of generative AI models are:
GANs have two neural networks - a generator and a discriminator. The first one creates synthetic data like text or images. The second one evaluates the generated data's authenticity against real examples. This process leads to continuous improvement in the generator output unless it produces only highly realistic content. They are used widely in different image generation tasks like enhancing visual data and creating photorealistic images.
Transformers have completely revolutionized NLP tasks. This is done by using self-attention mechanisms for processing and generating sequences of data. Its models like GPT can generate contextually relevant and coherent text as per the given input. Transformers are great in tasks like text summarization, dialogue generation and language translation.
VAEs work by understanding a latent representation of input data. It comprises an encoder and a decoder. The first one compresses the input data into a latent space. The second reconstructs data from the said latent space. This model introduces high randomness into the latent space. It enables new data sample generation, which mimics the original data distribution. They are used in anomaly detection, data compression and image synthesis.
Deep generative models bring together DL techniques with probabilistic modeling. The goal is to generate new data samples. Such models generally involve complicated training procedures and architectures to understand the underlying data structure. They are highly versatile and can be easily adapted to perform multiple generative tasks. These include video synthesis, reinforcement learning environments and image generation.
Auto-regressive models are used for generating output sequences, one element at a time. Each output relies on the prior generated elements. Models like Long Short-Term Memory and Recurrent Neural Network are under this category. They are useful for generating data sequences like music, time-series predictions and text. These models are highly capable of capturing temporal dependencies. They're used in tasks like music composition, handwriting generation and speech recognition.
Every generative AI model brings its specific applications and strengths to the table. They lead to distinct types of creative endeavors and generative tasks. The continuous advancements in the field of AI research are pushing their boundaries. Their achieving capabilities are being rewritten, promising more sophisticated and better generative capabilities in the coming years.
To summarize, AI generative models have transformed content production and innovation by allowing robots to produce realistic data. AI generative models, including GANs, VAEs, auto-regressive, and deep generative, have opened up various new possibilities. As we look to the future, AI generative models will keep influencing creativity and drive innovation in new manners.
Course Schedule
Course Name | Batch Type | Details |
Generative AI Training | Every Weekday | View Details |
Generative AI Training | Every Weekend | View Details |