The Power of Pre-training

Introduction

Imagine a child learning to read. Before they can decipher complex sentences, they spend years building a foundation of knowledge – recognizing letters, forming sounds, and understanding basic grammar. Similarly, pre-training plays a crucial role in artificial intelligence (AI), providing models with a foundational understanding of the world before tackling specific tasks

Pre-training, a popular paradigm in machine learning, involves training a model on a large dataset before fine-tuning it for a specific task. It has revolutionised various domains, from computer vision to natural language processing.

As per Yann LeCun "Pre-training is a key driver of progress in AI, allowing us to develop powerful models that can learn and adapt to new situations."

Deep learning is data intensive. in order to perform tasks like classification, prediction it needs lot of annotated data, which might not be present in some cases

In this article, we explore the benefits, challenges, and practical applications of pre-training.

1. Understanding Pre-training

Pre-training typically involves training a neural network on a massive dataset (often unsupervised) to learn useful features. These pre-trained models can then be fine-tuned on smaller, task-specific datasets. Here are some key points:

• Definition: Pre-training refers to the initial training phase where a model learns general features from a large dataset.
• Transfer Learning: Pre-trained models serve as a foundation for transfer learning, allowing us to leverage knowledge gained from one domain to improve performance in another.

2. Benefits of Pre-training

Let’s explore why pre-training is powerful:

a. Feature Extraction

• Rich Representations: Pre-training enables models to learn rich, hierarchical representations of data. For instance, pre-trained convolutional neural networks (CNNs) capture low-level features like edges and textures, which benefit downstream tasks.

b. Few-Shot Learning

• Generalisation: Pre-trained models generalise well even with limited labeled data. They act as knowledge repositories, reducing the need for extensive task-specific annotations.
• Fine-Tuning: Fine-tuning allows us to adapt pre-trained models to specific tasks efficiently.

3. Limitations and Considerations

While pre-training offers substantial advantages, it’s essential to acknowledge its limitations:

a. Domain Shift

• Covariate Shift: Linear regression under covariate shift exemplifies this challenge. The marginal distribution of input covariates differs between source and target domains, but the conditional distribution of output given input covariates remains similar. Pre-training followed by fine-tuning can mitigate this issue.

b. Data Efficiency

• Source Data Dependency: Pre-training often relies on abundant source data. However, fine-tuning with a small amount of target data can significantly reduce the need for extensive source data1.

4. Practical Examples

Let’s look at real-world examples:

a. Image Classification

• ImageNet Pre-training: Models pre-trained on ImageNet achieve impressive results across various image classification tasks. Fine-tuning on specific datasets (e.g., medical images) yields excellent performance.

b. Natural Language Processing (NLP)

• BERT: Bidirectional Encoder Representations from Transformers (BERT) pre-training revolutionised NLP. Fine-tuning BERT for sentiment analysis, question answering, or named entity recognition consistently outperforms traditional methods.

c. CLIP: Connecting Text and Images

• Description: CLIP is a neural network that efficiently learns visual concepts from natural language supervision. It can be applied to various visual classification tasks by providing the names of the visual categories to be recognised.
• Application: Imagine using CLIP to recognize objects in images based on textual descriptions. For instance, instructing CLIP to identify “a red apple” or “a snowy mountain.”
• Reference: Read more about CLIP.

d. Generative Pre-training from Pixels

• Description: Inspired by unsupervised representation learning for natural language, this approach trains a sequence Transformer to predict pixels without incorporating knowledge of the 2D input structure.
• Application: It can learn useful representations for images, even without explicit labels. Think of it as learning to generate meaningful image features from raw pixel data.
• Reference: Read the research paper.

e. Zero-Shot Transfer Learning with Pre-trained Models

• Description: Pre-trained models (like BERT or GPT-3) can be fine-tuned for specific tasks without directly optimising for the benchmark’s performance. They generalise well across different tasks.
• Application: Using a model pre-trained on one task (e.g., text classification) to perform well on another task (e.g., sentiment analysis) without extensive task-specific annotations.

Reference: Learn more about zero-shot capabilities.

Success Story: Google's BERT Model: Pre-trained on a massive corpus of text data, BERT revolutionized the field of natural language processing (NLP). It achieved state-of-the-art performance in various NLP tasks, including sentiment analysis, question answering, and text summarization.

Cautionary Tale: Tay, Microsoft's Chatbot: Launched in 2016, Tay quickly learned to generate offensive and harmful language after being exposed to user-generated content on Twitter. This highlights the importance of carefully selecting and filtering pre-training data to avoid unintended consequences.

APPCAIR IEEE AI Symposium

Had an opportunity to attend session from Prof Niloy Ganguly of Indian Institute of Technology, Kanpur where he highlighted some of the work done to tackle several problems related to pre-training. Especially the use cases on crystals , genes and NLP were interesting. He also spoke about domain specific pre-training in several NLP domains. Some key learnings are

• Domain Specific datasets are small in size and costly to make as heavy domain knowledge is needed. It is un-reliable when annotated on large scale (crowdsourced datasets). hence leveraging available unlabelled data is important
• We should circumvent the need of Deep-learning models to have annotated data by understanding the semantics of unannotated data(Ex. Read lot of story books to enable writing good essays. here both tasks are independent)
• To leverage unannotated dataset, perform a simple task of self supervision on millions/billions of dataset and some sort of understanding will emerge

• Domain specific pre-training fine tuning model significantly improves performance
• BioBERT is the first domain specific BERT based model pretrained on biomedical corpora for 23 days on 8 英伟达 V100 GPUS
• Masked language model assume that each sentence and document hosting them are independent entity. but in practice it is no so. so we can leverage document level similarity and their categorisation for pre-training
• Frugal pre-training leveraging document level semantics shows dramatic improvements on several domains

• When semantics is not available, non language strings (genes) can be used

Conclusion

"The success of pre-training highlights the importance of foundational knowledge in AI, just like it is essential for human learning." - Fei-Fei Li , Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI)

Pre-training empowers machine learning practitioners by providing robust feature representations and enabling efficient transfer learning. But this research is constantly evolving. As these advancements continue, we can expect pre-training to play an even more critical role in unlocking the full potential of AI in the years to come.

By understanding the power and limitations of pre-training, we can develop and deploy AI models responsibly and ethically, paving the way for a future where AI benefits all of humanity.

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Department of CSIS BITS Pilani Goa Campus Birla Institute of Technology and Science, Pilani Research & Innovation, BITS Pilani BITS Pilani, Hyderabad Campus Director BITS Pilani - K.K. Birla Goa Campus Prof. V Ramgopal Rao