What are the most commonly used datasets for training generative models?

Harness, Train, Create! ???? I'd utilize image datasets like ImageNet or COCO for rich visual training. Text datasets, including Project Gutenberg or Wikipedia dumps, provide diverse linguistic patterns. Audio datasets such as LibriSpeech or Freesound offer a range of sounds and spoken words. Each dataset type, referenced in "Deep Learning" by Ian Goodfellow and Yoshua Bengio, fuels the model's ability to generate new, innovative outputs, pushing the boundaries of creativity and functionality in engineering applications.

For any problem which is to be solved by any type of AI traing is one of the most important aspects hence if we consider the traing data one of the prominent data is vision data ( images, video, Lidars etc.). For any AI algorithm involving any form of detection in an image or video the extensive traing data of images are required. Not only images but one Important aspect is quality images as the algorithms work based on features extracted and better the image more features can be identified and extracted.

Generative models in image processing use key datasets. ImageNet offers over 14 million images for object recognition. COCO provides scenes for object detection and captioning. MNIST, with its simple handwritten digits, is great for basic learning. CelebA's celebrity images aid in face recognition, and LSUN's scene categories help in understanding complex environments. These datasets are vital for training diverse visual recognition models.

I would recommend considering the ethical implications of generative models in AI, particularly in image synthesis. While these models offer significant advancements in technology and creativity, they also pose challenges related to bias, privacy, and the potential for misuse. It's crucial to develop and adhere to ethical guidelines that ensure the responsible use of this technology. Balancing innovation with ethical considerations is key to harnessing the full potential of AI in a way that benefits society and respects individual rights.

The CelebA, LSUN and CIFAR-10 datasets have become standardized for developing and evaluating generative models, particularly generative adversarial networks, thanks to their large size and coverage of diverse image classes. CelebA's facial images help generative models learn robust facial feature representations. LSUN's scene and object photos from a range of environments enable modeling background context. And CIFAR-10, while low resolution, forces models to infer fine-grained details that capture natural image properties. Together these datasets spur innovation by providing ample training data across facial, object and scene domains.

Similar to image/vision data kne other important data type is text data. Which powers numerous AI platforms and products. It's all possible only after hours of rigorous traing of text data to built rules and logic through which AI engines can read, extract and interpret the data. Text data is an important source of AI based engines to analyze and report feedbacks and sentiments across platforms of customers for a particular service or project.

People talk about native web-based text data, but transcriptions of videos are a massively overlooked source of data, as are transcriptions of podcasts. As with most training data, keep bias in mind. Also keep in mind that a large proportion of text data on the web is factually incorrect or subjective, which can have a big impact on model performance. Less data of higher quality is better than more data of lower quality.

PTB is ideal for language modeling with clean sentences but lacks sentiment. Yelp suits sentiment analysis and style transfer but may not generalize. WikiText is vast and diverse, great for text generation but demands substantial computational resources. Choose based on task-specific needs.

Text datasets like AG News, IMDb Reviews, SNLI, PubMed, WikiText, and Quora Question Pairs serve diverse NLP tasks. AG News for news categorization, IMDb for sentiment analysis, SNLI for inference, PubMed for biomedical literature, and WikiText for language modeling. Quora aids in duplicate question detection.

The Penn Treebank, Yelp reviews, and WikiText corpora have become widely adopted benchmarks thanks to their scale and diversity of textual content. Penn Treebank encompasses over a million words across Wall Street Journal articles - a vast corpus covering news, finance and more. Yelp's millions of customer reviews with sentiment ratings enable generating text conditional on affect. And WikiText spans a hundred million words across Wikipedia's broad topical range. Together they provide ample training data for developing generative models that can produce meaningful, multipurpose text - whether unconditionally or conditioned on attributes like sentiment or topic.

When working with generative models on audio datasets: - Use spectrogram representations to capture time and frequency information. - Apply data augmentation techniques to enhance model generalization. - Consider conditional generation for more controlled audio synthesis. - Explore transfer learning with pre-trained models for efficiency. - Evaluate generated audio using perceptual quality metrics for realistic results. By incorporating these tips, the training and application of generative models for audio synthesis can be optimized for diverse and realistic sound generation.

The application of generative models in this domain is exemplified by datasets like NSynth, a treasure trove of over 300,000 musical notes from 1,000 instruments, annotated with labels like pitch, timbre, and velocity. LibriSpeech contributes to this sonic innovation with 1,000 hours of read speech from audiobooks and corresponding transcripts. Meanwhile, UrbanSound8K, with its 8,732 sound clips across 10 urban sound classes, ranging from dog barks to car horns, emerges as a versatile resource, catering to both sound classification and generation. This convergence of datasets and generative models propels the synthesis of audio into a realm of boundless creativity and innovation.

In LegalTech, audio datasets like court recordings or legal discussions can be pivotal for training generative models in speech synthesis. For example, a dataset comprising various legal proceedings and attorney-client meetings, annotated for clarity, emotion, and legal terminology, can be used to generate synthetic speech samples. This assists in creating realistic training environments for speech recognition systems used in legal settings, enhancing their ability to accurately transcribe and analyze legal discussions. Such tailored audio datasets not only improve the quality of synthetic speech generation but also ensure the models are finely tuned for the specific acoustic and linguistic nuances of the legal domain.

Audio datasets for training generative models: - LibriSpeech: 1,000 hours of clean speech, ideal for speech synthesis and voice cloning - AudioSet: 1M YouTube videos with diverse sounds, great for music generation & sound effect creation - ESC-50: 2,000 environmental sound recordings, perfect for training models to generate realistic soundscapes - FSD10k: 200k audio clips with 10k sound classes, ideal for broad audio classification & generation tasks - VoxCeleb: Over 1 million utterances from 6,121 speakers, ideal for voice identification & speaker-specific speech synthesis Choosing the right dataset depends on your desired audio output & model application. Dataset quality and size are crucial factors for successful model training.

It may seemed to be funny but Audio data is also one of the most popular source of training data which fuels multiple AI ecosystems. For example: Alexa, Google, voice comand based smart home equipments. All these require and are getting trained by voice data of thousands and millions of users. Transcripting voice messages and personalized voice recognition programs are prominent examples of use cases or pre trained voice data sets.

We can also consider artificial datasets which are created by AI or labelled and annotated by AI. As scale is the prime factor in accuracy and performance of AI algorithms. It's very hard to get quality data as well as quantity data of medical images so in that case we have to create artificial datasets to improve performance of Models. But we also have to careful about while using and creating such types of datasets. Because there is high chance of mistakes in it.

Comprising several scene categories like bedrooms, kitchens, etc., Large-scale Scene UNderstanding (LSUN) has been pivotal in my work on environmental scene generation. The dataset's variety and volume provided a rich training ground for our generative models to accurately generate diverse scene layouts. While not a single dataset, the data used to train models like GPT ((Generative Pre-trained Transformer) includes a vast array of internet text. In a project to develop an advanced language model, the diverse nature of the GPT datasets allowed for a nuanced understanding of language and syntax.

To start with the datasets that are commonly used for training generative AI models, are each tailored to specific applications and data types. - For text generation, Penn Treebank (PTB) and BookCorpus are widely used while, - CIFAR-10 and ImageNet are used for image classification and generation tasks. - For audio processing, Free Music Archive (FMA) and Common Voice are popular choices, and - MNIST, Speech Commands Dataset, and Flickr Audio Caption Corpus are preferred for multimodal applications.

I would suggest adding information on the process of dataset creation and curation for generative models. This includes the source selection, criteria for inclusion, and methods used to ensure diversity and reduce biases. Highlighting these aspects is crucial as they directly impact the model's output quality and ethical implications. Understanding the origins and composition of datasets like NSynth, LibriSpeech, or UrbanSound8K helps in assessing their limitations and potentials. Discussing these factors provides a deeper insight into the challenges and considerations in developing effective and responsible generative models.

Beyond the raw data types, it’s important to consider other factors when training generative models. These include dataset biases, ethical considerations, licensing, and the intended application of the model. Models are only as good as the data they’re trained on, so ensuring a dataset’s integrity and representativeness is paramount. Additionally, the evolution of generative AI demands ongoing scrutiny of the datasets used, with a focus on improving diversity, fairness, and openness.