1. Text Generation (e.g., GPT, T5, BERT)

  • Data Type: Large collections of text data, such as books, articles, websites, and dialogues.
  • Goal: model learns language patterns, grammar, vocabulary, and contextual relationships between words, sentences, and paragraphs.

    • Examples

  • Text corpora like Wikipedia, Common Crawl, BooksCorpus, or specialized datasets for domains (e.g., medical texts, legal documents).
  • Specific datasets tailored to tasks, like SQuAD (for question answering) or CoNLL (for named entity recognition).

    • Key Characteristics

  • Diversity: The model can generalize better if exposed to diverse topics, writing styles, and contexts.
  • Balance: should ideally represent the distribution of topics the model is expected to handle.

2. Image Generation (e.g., GANs, VAEs, DALL·E)/h2>

  • Data Type: Large collections of images, often paired with labels or captions (for conditional models).
  • Goals The model learns the distribution of pixel patterns and high-level features (e.g., objects, textures, and layouts).

    • Examples

  • ImageNet (general image classification), MS COCO (images with captions for image captioning tasks), CelebA (celebrity faces dataset), LSUN (scene and object category images).
  •  For more specialized applications, datasets could include medical imaging (e.g., X-ray images) or satellite imagery

    • Key Characteristics

  • Resolution: High-resolution images generally lead to better performance, but they require more computation.
  • Labeling: For tasks like image captioning or object detection, data needs to be labeled with corresponding tags or bounding boxes.

3. Audio Generation (e.g., WaveNet, Tacotron, Jukedeck):

  • Data Type: files, typically paired with text for tasks like speech synthesis or music generation.
  • Goal: The model learns the patterns in sound waves, phonetics, melodies, or speech prosody.

    • Examples

  • LibriSpeech (speech-to-text), VoxCeleb (celebrity speech), GTZAN (music genre classification), or datasets with spoken dialogues like Switchboard.
  •  Music generation models are often trained on datasets like MAESTRO (piano performances) or Million Song Dataset (music data).

    • Key Characteristics

  • Music generation models are often trained on datasets like MAESTRO (piano performances) or Million Song Dataset (music data).
  •  Clean Data: Audio data should be clean, meaning free of noise or distortions that could confuse the model, unless noise is a specific use case (e.g., noise-robust models).

4. Video Generation (e.g., MoCoGAN, TGAN):

  • Data Type: Sequences of images or videos, often in the form of frames or motion sequences.
  • Goal: model learns the temporal patterns and dependencies between frames (motion, transitions, and dynamics).