When it comes to training models, there is a perception that more data is better. However, in practice, data is not necessarily a silver bullet. Recently, there has been a growing interest in few shot learning methodologies to learn with limited data.
In the rapidly evolving field of artificial intelligence, learning from limited data and Ability to generalize이 중요해졌습니다. In the post below, I'll explain why the few shot learning methodology can be a promising solution for training AI models.
Understanding few shot learning
Definition of Few shot learning
Few shot learning refers to a machine learning methodology that can learn from a limited number of samples, less than 10. 보통 deep learning model is pre-trained on a large amount of data, then fine-tuned on a few samples for a new task.
Machine learning aims to bridge the gap between human-like learning capabilities. The idea is that just as humans can quickly pick up new concepts with just a few examples, so can models.
Key challenges and goals
Few shot learning needs to address issues such as the lack of labeled training data, the need for effective representation learning, and the ability to generalize to new unseen classes;
Few shot learning has the advantage of reducing the amount of data needed to train a model, which can reduce the cost of training and deploying the model. It can also increase the flexibility and adaptability of the model because it can more easily adapt to new tasks.
This methodology is based on the NLP and It can be applied to a variety of areas, such as computer vision.
Few shot learning vs. Zero shot learning comparison
Categories or labels that a machine learning model has never seen or learned about before are called "classes". Zero shot learning (ZSL) is a machine learning methodology that allows a model to learn to recognize a new class without ever seeing an example of it during training, meaning that the model can be taught to recognize and classify it accurately using information about its attributes or characteristics without explicit training on the new class.
ZSL leverages semantic embedding and feature-based learning to optimize the learning process. Here's a closer look at these principles
- Semantic embedding is the mapping of words or phrases to a vector space, which Natural Language Processing technique.
- The distance between vectors reflects the semantic similarity between words or phrases.
- 일반적으로 Use this as a way to cope with large amounts of data without labeling.
- You can use previously trained models to solve new problems.
Zero-shot learning, with its ability to learn from a new class, has been used in many areas, including image recognition and natural language understanding. It's being utilized in many applications, including information retrieval, text categorization, and translation. For example, AirBnB uses zero shot learning to predict guest preferences and recommend listings that match them.
The most notable differences between the zero-shot learning and few-shot learning models are as follows.
1. select Data
- FSL: Need a small amount of labeled examples
- ZSL: No labeled examples required
2. Applications
- FSL: Used for image classification, NLP, reinforcement learning, and more.
- ZSL: Typically useful for tasks that involve understanding or generating text, such as answering questions and summarizing.
3. algorithm
- FSL: Uses meta learning, a technique for learning how to learn in general.
- ZSL: Based on a generation model that can generate new data
4. performance
- FSL: The limit of high performance with a small number of examples, but less accurate than traditional machine learning algorithms trained on large data sets.
- ZSL: Can achieve high accuracy but is vulnerable to noise or outliers in the data
Based on the above, let's look at an example. Few shot learning looks at a few labeled examples and determines that different types of flowers We will be able to train a model to classify the image. On the other hand, a zero-shot learning model would be able to translate text from English to French without training on French data, such as can be used to build models. While there are similarities, these are distinctly different models with different strengths and limitations, so you'll need to refine the problem or goal you want to solve with your AI model before deciding which technique to use.
Few shot learning principle
Approach
The few shot learning methodology can be broken down into three approaches.
- Metric-based methods: Utilize similarity metrics to compare and separate new instances based on their similarity to a few labeled examples. Prototype networks and matching networks are among the best examples of metric-based few shot learning.
- Model-based methods: Aim to learn a generative model from available labeled examples and then generate new samples in a few-shot learning scenario. Typically, they use variational autoencoders (VAEs) and Generative Adversarial Networks (GANs) use for model-based few shot learning.
- Optimization-based methods: Optimization-based methods formulate prime number learning as an optimization problem, where the model tunes its parameters to fit the few training examples available. Methods like model-agnostic meta-learning (MAML) and Reptile fall into this category.
How to learn
Few shot learning can usually be categorized in four ways.
- Memory-based methods
- Use the memory of the trained model to learn new tasks
- If it has learned to distinguish between dogs and cats, it can have a memory containing images of dogs and cats
- When it sees a new image, the model can use its memory to decide whether the image is of a dog or a cat.
- Use pre-trained models
- Models that have already been trained on large data sets
- Pre-trained models can be fine-tuned and adapted to new tasks
- Regulated learning
- used to prevent errors while the model is learning
- Can be used to avoid over-fitting the training data
- Reinforcement learning
-allows the model to learn from its mistakes
-allows the model to teach itself how to perform new tasks.
Few shot learning advantages
Few shot learning typically involves pre-training a model on a dataset with lots of labeled data, and then How to learn new tasks with a small amount of data.
It is a model that contrasts with the traditional approach of deep learning. Few shot learning is currently active in a variety of fields, including computer vision, natural language processing, and robotics, as opposed to traditional models that use many examples in a training data set to learn.
In the early 2000s, researchers were working on ways to perform classification, regression, and clustering tasks with little training data. With the increasing availability of large computer vision datasets and advances in deep learning, advances in few shot learning have increased significantly.
FEW SHOT LEARNIG is used to create a model that can learn a task with just a few shots. A typical usage example is shown below.
- With just a few images, A computer vision system that can identify new objects.
- Natural language processing systems that can translate new languages with just a few sentences
- Robotic systems that can perform new tasks with a few examples
Few shot learning requires less data to learn a new task than traditional machine learning methodologies, which can be useful for certain tasks where data collection can be expensive and time consuming.
It's also worth noting that if your new task changes frequently, you'll be able to adapt more quickly. Similarly, if you need to train a model on different kinds of data, it can generalize faster than traditional methodologies.
Few shot learning Use case
Applications
Few shot learning is used in many areas, including computer vision, speech technology, and natural language processing.
- Computer vision
Image classification: Detect unseen Image classification
Face recognition: Extracting basic features of an individual without any training
Image-to-Image Conversion: Converts the style of an unknown image to a known image style.
- Speech Technology
Speech Transformation: Transforms Change speaking style
Classify sound events: Classify unobserved sound events
Emotion classification: Classify unobserved emotions in speech.
- Natural Language Processing (NLP)
Text classification: The ability to classify unobserved text classes.
Apple uses few shot learning to learn your usage patterns. When Siri is asked a question for the first time, it uses a few examples of similar questions to learn how to answer new questions. This allows Siri to provide more accurate and relevant answers, even when asked a question she hasn't heard before.
In addition, Apple uses few shot learning to recommend content that's right for you. When a user downloads an app, the App Store uses a few examples of other apps the user has downloaded to learn about the user's interests; a news app uses a few examples of other articles the user has read to learn about the user's interests. Few shot learning allows Apple to deliver personalized content to users in a more efficient and effective way. By learning from a small number of examples and applying that knowledge to a much larger number of users, users are more personalized experiences and engagement and satisfaction.
Latest trends
Recent trends in machine learning have focused on improving the speed of models and the quality of training data. For example, large language models (LLMs) are trained on vast amounts of data, which can help few shot learning models learn new tasks quickly. In addition, there is a growing use of hardware acceleration, which is used to speed up the learning and inference of FEW SHOT LEARNING models.
There has also been a growing movement to utilize few shot learning models in businesses where large amounts of data cannot be built up. Researchers at Stanford University are using few shot learning to develop an AI system that can diagnose skin cancer from a small number of images. In the medical field, the absolute lack of data to train a model has been a barrier, but if a small amount of data is accurate and of high enough quality for the model to analyze and recognize, a few shot learning model can be used to create a good enough AI system.
At DataHunt, we believe that the quality of training data for a few shot learning model should be as follows
- The data should contain all the information needed to train the model: both positive and negative examples, and examples from different classes.
- This is a very important criterion because errors in the data can lead to errors in the model.
- The data your model will use must be representative of the real world. If the data doesn't resemble the real world, the model won't generalize well to new data.
At DataHunt, we believe that when you need a small amount of data, such as for a few shot learning model, you need more stringent data quality standards than you're used to. So, rather than letting AI do all the training data processing, let it be AI assisted, but Human in the loop (HITL) to ensure that the data always goes through the hands of a human operator. AI and humans collaborate on tasks like removing data errors, outliers, and duplicate data, as well as labeling to improve accuracy. After being reviewed by experts in tasks such as improving data diversity and quality frameworks, we are able to process training data with 99% accuracy.
Few shot learning limitations and the future
Few shot learning is still in the early stages of development, but it has already been applied to a variety of tasks with success. The methodology will continue to evolve and has the potential to replace traditional machine learning models. Of course, there are challenges to overcome.
Few shot learning limitations
There's a saying that goes, "A dog can recite a lunar calendar in three years. Generally speaking, humans can easily learn a new task with a few experiences. However, training a model requires a lot of data. However, in data-poor tasks such as medical diagnosis or natural language processing, the process of building a large amount of data can cause many problems.
Even if you've built up a lot of data, you may hit a wall when it comes to processing resources, which can be costly and time-consuming for real-time applications.
Few shot learning models can be a way to address these issues, but they are also criticized for having similar limitations. In addition to the problems with the general model, there are additional limitations of the Few shot learning model.
- Task transferability: Few shot learning models are typically trained on a specific task, such as image classification or natural language translation, which means they may not generalize to other tasks, even if they are similar.
- Model complexity: Few shot learning models can be complex, which can make them difficult to understand and explain. This can be a problem in applications where transparency is important, such as healthcare or finance.
The future of few shot learning as seen through the eyes of GPT
GPT-4 utilizes a transformer architecture that evaluates the relevance of words in a sequence when generating the next word. The model is trained on a large corpus of internet text, but it differs from other language models in that it utilizes episodic learning for inference.
Some examples, called 'prompts', can be found in GPT-4 serves as a demonstration of the challenges to be solved. It doesn't break down the learning process into training and inference phases, but rather considers past and present input prompts as part of an ongoing conversation and predicts the next word in the sequence.
ChatGPT is an example of how efficiently few shot learning can be implemented. When interacting with ChatGPT, we type conversational prompts, which the model processes along with pre-trained data to generate responses. Each user's input and model output are then added to the conversation history. The model utilizes this to generate relevant responses.
However, the limitations of this methodology can also be found in ChatGPT. While few shot learning is great at quickly adapting to new tasks, it still tends to rely on the knowledge that the model gained during the pre-training phase. As a result, it cannot generate accurate answers to topics or facts that are not included in the training dataset, or that were introduced to the world after the training cutoff (for example, ChatGPT doesn't know about content after September 2021). Also, because they rely on statistical patterns learned from training data rather than an understanding of the real world, they often It could also generate inaccurate or misleading answers.
Conclusion: Raw data is critical for effective few shot learning models
Through the example of ChatGPT, we can see the fundamental limitations of few shot learning. These are the need for high-quality training data, even if the amount is small, and the high probability of overfitting when training on an insufficient sample.
However, there are also a number of things to look forward to with few shot learning. First, it minimizes the chance of overfitting to the training data and failing to generalize to new data. This happens because the model learned about the details of the training data, and then failed to learn about the patterns in the new data.
The bottom line is that few shot learning can work better if the original data you initially train on is of high quality, as it allows you to capture the accuracy of the model in the initial training phase. This is why DataHunt puts so much effort into ensuring the quality of the data our models first encounter.
Few shot learning promises a future where AI can learn and adapt more efficiently, even when faced with sparse data or new tasks. It's important to note the need for accurate semantic representation and to be wary of biased views, but it can be a useful methodology for learning new tasks with little training data, so it's definitely worth exploring in the future.
Bottom line:
- Few shot learning is a machine learning methodology that can learn from a limited sample, allowing the model to generalize and make accurate predictions in situations where data is scarce.
- It allows us to identify new objects/languages/etc. with just a few images and perform tasks, and is used in a variety of fields including computer vision, natural language processing, and robotics.
- As you can see from the ChatGPT example, Few shot learning is most efficient when it takes the quality and accuracy of the original data, as it performs the following actions based on existing training data.
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