Pretraining and fine-tuning are essential techniques in machine learning that enable models to learn from large datasets and adapt to specific tasks.
Pretraining involves training a model on a large dataset to learn general features and representations. This process helps the model capture the underlying structure of the data and develop a strong foundation for further learning. Fine-tuning, on the other hand, involves adapting the pretrained model to a specific task using a smaller, task-specific dataset. This process allows the model to refine its knowledge and improve its performance on the target task.
Recent research has explored various strategies to enhance the effectiveness of pretraining and fine-tuning. One such approach is the two-stage fine-tuning, which first fine-tunes the final layer of the pretrained model with class-balanced reweighting loss and then performs standard fine-tuning. This method has shown promising results in handling class-imbalanced data and improving performance on tail classes with few samples.
Another notable development is the cross-modal fine-tuning framework, ORCA, which extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA aligns the embedded feature distribution with the pretraining modality and then fine-tunes the pretrained model on the embedded data, achieving state-of-the-art results on various benchmarks.
Moreover, researchers have investigated the impact of self-supervised pretraining on small molecular data and found that the benefits can be negligible in some cases. However, with additional supervised pretraining, improvements can be observed, especially when using richer features or more balanced data splits.
Practical applications of pretraining and fine-tuning include natural language processing, computer vision, and drug discovery. For instance, pretrained language models have demonstrated outstanding performance in tasks requiring social and emotional commonsense reasoning. In computer vision, hierarchical pretraining has been shown to decrease convergence time, improve accuracy, and enhance the robustness of self-supervised pretraining.
In conclusion, pretraining and fine-tuning are powerful techniques that enable machine learning models to learn from vast amounts of data and adapt to specific tasks. Ongoing research continues to explore novel strategies and frameworks to further improve their effectiveness and applicability across various domains.
Pretraining and Fine-tuning
Pretraining and Fine-tuning Further Reading
1.Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data http://arxiv.org/abs/2207.10858v1 Taha ValizadehAslani, Yiwen Shi, Jing Wang, Ping Ren, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang2.Cross-Modal Fine-Tuning: Align then Refine http://arxiv.org/abs/2302.05738v2 Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar3.Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense http://arxiv.org/abs/2105.05913v1 Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, Dilek Hakkani-Tur4.DP-RAFT: A Differentially Private Recipe for Accelerated Fine-Tuning http://arxiv.org/abs/2212.04486v2 Ashwinee Panda, Xinyu Tang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal5.Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes http://arxiv.org/abs/2211.13638v1 Yiqiao Jin, Xiting Wang, Yaru Hao, Yizhou Sun, Xing Xie6.Multi-pretrained Deep Neural Network http://arxiv.org/abs/1606.00540v1 Zhen Hu, Zhuyin Xue, Tong Cui, Shiqiang Zong, Chenglong He7.Extending the Subwording Model of Multilingual Pretrained Models for New Languages http://arxiv.org/abs/2211.15965v1 Kenji Imamura, Eiichiro Sumita8.Downstream Datasets Make Surprisingly Good Pretraining Corpora http://arxiv.org/abs/2209.14389v1 Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham, Zachary C. Lipton9.Does GNN Pretraining Help Molecular Representation? http://arxiv.org/abs/2207.06010v2 Ruoxi Sun, Hanjun Dai, Adams Wei Yu10.Self-Supervised Pretraining Improves Self-Supervised Pretraining http://arxiv.org/abs/2103.12718v2 Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shanghang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, Trevor DarrellPretraining and Fine-tuning Frequently Asked Questions
What is the difference between pretraining and fine-tuning?
Pretraining and fine-tuning are two essential techniques in machine learning that enable models to learn from large datasets and adapt to specific tasks. Pretraining involves training a model on a large dataset to learn general features and representations, capturing the underlying structure of the data. Fine-tuning, on the other hand, involves adapting the pretrained model to a specific task using a smaller, task-specific dataset. This process allows the model to refine its knowledge and improve its performance on the target task.
What does pretraining mean?
Pretraining is a technique in machine learning where a model is initially trained on a large dataset to learn general features and representations. This process helps the model capture the underlying structure of the data and develop a strong foundation for further learning. Pretraining is often used as a starting point for fine-tuning, where the model is then adapted to a specific task using a smaller, task-specific dataset.
What does pretraining a model mean?
Pretraining a model means training the model on a large dataset before adapting it to a specific task. This initial training helps the model learn general features and representations, capturing the underlying structure of the data. Pretraining provides a strong foundation for further learning, allowing the model to be fine-tuned on a smaller, task-specific dataset to improve its performance on the target task.
What is pre-training and fine-tuning in NLP?
In natural language processing (NLP), pretraining and fine-tuning are techniques used to train models on large text corpora and adapt them to specific tasks. Pretraining involves training a model on a large text corpus to learn general language features and representations. Fine-tuning, on the other hand, involves adapting the pretrained model to a specific NLP task, such as sentiment analysis or machine translation, using a smaller, task-specific dataset. This process allows the model to refine its knowledge and improve its performance on the target task.
How do pretraining and fine-tuning improve machine learning model performance?
Pretraining and fine-tuning improve machine learning model performance by leveraging the knowledge gained from large datasets and adapting it to specific tasks. Pretraining helps the model learn general features and representations from a large dataset, capturing the underlying structure of the data. Fine-tuning then refines the model's knowledge using a smaller, task-specific dataset, allowing it to perform better on the target task. This combination of techniques enables models to benefit from both the vast amounts of data available for pretraining and the specialized knowledge required for specific tasks.
What are some recent advancements in pretraining and fine-tuning techniques?
Recent advancements in pretraining and fine-tuning techniques include two-stage fine-tuning, which first fine-tunes the final layer of the pretrained model with class-balanced reweighting loss and then performs standard fine-tuning. This method has shown promising results in handling class-imbalanced data and improving performance on tail classes with few samples. Another notable development is the cross-modal fine-tuning framework, ORCA, which extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA aligns the embedded feature distribution with the pretraining modality and then fine-tunes the pretrained model on the embedded data, achieving state-of-the-art results on various benchmarks.
What are some practical applications of pretraining and fine-tuning?
Practical applications of pretraining and fine-tuning include natural language processing, computer vision, and drug discovery. In NLP, pretrained language models have demonstrated outstanding performance in tasks requiring social and emotional commonsense reasoning. In computer vision, hierarchical pretraining has been shown to decrease convergence time, improve accuracy, and enhance the robustness of self-supervised pretraining. In drug discovery, researchers have investigated the impact of self-supervised pretraining on small molecular data and found that the benefits can be negligible in some cases. However, with additional supervised pretraining, improvements can be observed, especially when using richer features or more balanced data splits.
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