Pre-training on datasets such as ImageNet-21K, BookCorpus, and Common Crawl is standard practice, followed by fine-tuning to improve model performance. Parameter-efficient fine-tuning techniques include LoRA, which modifies lower-rank matrices, and adapters that insert modules to minimize parameter tuning. Visual prompt tuning introduces learnable parameters while keeping the backbone model frozen. Convolutional models, fundamental in image feature extraction, demand fewer resources than transformer-based methods. Discriminative models classify data instances, while generative models create them, both essential in machine learning.
Pre-training on large datasets like ImageNet-21K and fine-tuning for specific tasks enhances model convergence and performance.
Parameter-efficient fine-tuning methods like LoRA, adapter, VPT, and SSF improve efficiency and performance in various applications.
Convolutional architectures, historically used in image feature extraction, require fewer resources and offer good generalization compared to transformer-based models.
Discriminative models focus on distinguishing between data instances, while generative models aim to create new data instances.
#pre-training #fine-tuning #machine-learning #convolutional-models #discriminative-and-generative-tasks
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