Machine learningDeep learning / NLP / CV

Fine-Tuned Transformer

Fine-tuning a Transformer adapts a large pre-trained model — such as BERT, GPT, or ViT — to a specific downstream task by continuing gradient-based training on a labelled target dataset. This two-stage paradigm (pre-train then fine-tune) consistently achieves state-of-the-art results across NLP and computer vision tasks with far less task-specific data than training from scratch.

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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. link ↗
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171–4186. link ↗

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Fine-Tuned BERT-based Classification Fine-Tuned GRU Fine-Tuned LSTM Fine-Tuned Multilayer Perceptron Fine-Tuned Recurrent Neural Network Fine-Tuned Reinforcement Learning Fine-Tuned RoBERTa-based Classification Fine-Tuned Sentence Embeddings Fine-Tuned Variational Autoencoder Self-supervised Transformer Semi-supervised Transformer Weakly supervised transformer

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