Machine learning methods optimized and applied to multi-task learning

Multi-task learning is a model that is jointly optimized for multiple tasks, where related tasks share representations and improve model performance by learning better decision boundaries on the original tasks. A single neural network is often used to solve multiple tasks simultaneously. In addition to reducing inference time, jointly solving task groups has other benefits, such as improved prediction accuracy, improved data efficiency, and reduced training time.

What is a multi-task learning model?

Multi-task learning means that a machine learning model can handle multiple different tasks at the same time. It can improve data utilization efficiency, speed up model convergence, and reduce overfitting problems because models can share representations.

Multi-task learning is more similar to human learning mechanisms because humans often learn transferable skills. For example, after you learn to ride a bicycle, it becomes easier to learn to ride a motorcycle. This is called inductive transfer of knowledge.

This knowledge transfer mechanism allows humans to learn new concepts with only a few examples or no examples, which are called "small sample learning" and "zero sample learning" in machine learning respectively. .

Optimization method of multi-task learning

Not all tasks are related, the imbalance of the data set, the difference between tasks, Negative transfer of knowledge poses challenges to multi-task learning. Therefore, optimization of the task is as important as choosing the appropriate architecture. Next we discuss optimization strategies for multi-task learning.

1. Loss Construction

This is one of the most intuitive ways to perform multi-task optimization, by using different weighting schemes to balance the individual A single loss function defined by the task. The model then optimizes an aggregate loss function as a way to learn multiple tasks at once.

For example, using different loss weighting mechanisms to help solve multi-task problems. The specific weight assigned to each loss function is inversely proportional to the training set size of each task to avoid letting tasks with more data dominate the optimization.

2. Hard parameter sharing

In hard parameter sharing, the hidden layers of the neural network are shared while retaining some task-specific output layer. Sharing most of the layers for related tasks reduces the possibility of overfitting.

The more tasks a shared model learns simultaneously, the more necessary it is to find a representation that captures all tasks, and the less likely it is that the original task will be overfitted.

3. Soft Parameter Sharing

Hard parameter sharing only performs well when the tasks are closely related. Therefore, the focus of soft parameter sharing is to learn features that need to be shared between tasks. Soft parameter sharing refers to regularizing the distance between the parameters of each model and the overall training target to encourage the use of similar model parameters between different tasks. It is often used in multi-task learning because this regularization technique is easy to implement.

4. Data Sampling

Machine learning data sets are often affected by imbalanced data distribution, and multi-task learning further complicates this problem . Because multi-task training datasets with different sizes and data distributions are involved. Multi-task models are more likely to sample data points from tasks with larger available training datasets, leading to potential overfitting.

To deal with this data imbalance, various data sampling techniques have been proposed to correctly construct training datasets for multi-task optimization problems.

5. Intelligent task scheduling

Most multi-task learning models decide which tasks to train in an epoch in a very simple way, either Either train on all tasks at each step, or randomly select a subset of tasks for training. However, intelligently optimized task scheduling can significantly improve the overall model performance for all tasks.

6. Gradient Modulation

Most multi-task learning methods assume that the individual tasks of joint optimization are closely related. However, each task is not necessarily closely related to all available tasks. In this case, sharing information with unrelated tasks may even harm performance, a phenomenon known as "negative transfer."

From an optimization perspective, negative migration manifests itself as conflicting task gradients. When the gradient vectors of two tasks point in opposite directions, the gradient of the current task degrades the performance of the other task. Following the average of the two gradients means that neither task sees the same improvement as the single-task training setting. Therefore, modulation of task gradients is a potential solution to this problem.

If a multi-task model is trained on a set of related tasks, then ideally the gradients for these tasks should point in similar directions. A common way of gradient modulation is through adversarial training. For example, the gradient adversarial training (GREAT) method explicitly enforces this condition by including an adversarial loss term in multi-task model training, which encourages gradients from different sources to have statistically indistinguishable distributions.

7. Knowledge distillation

Knowledge distillation is a machine learning paradigm in which knowledge is transferred from a computationally expensive model (the "teacher" model) to a smaller model (the "student" model) while maintaining performance.

In multi-task learning, the most common use of knowledge distillation is to extract knowledge from several separate single-task "teacher" networks into a multi-task "student" network. Interestingly, the performance of student networks has been shown to exceed that of teacher networks in some areas, making knowledge distillation an ideal approach to not only save memory but also improve performance.

Practical Applications of Multi-Task Learning

Researchers in all fields of artificial intelligence use multi-task learning frameworks to develop resource optimization models that are reliable and Multi-task models can be used in multiple application areas with storage constraints. Let’s take a look at the latest applications of these models in different areas of artificial intelligence.

1. Computer Vision

Computer vision is a branch of artificial intelligence that deals with issues such as image classification, object detection, and video retrieval. Most single-task computer vision models are computationally expensive, and using multi-task networks to handle multiple tasks can save storage space and make them easier to deploy in more real-world problems. Furthermore, it helps alleviate the problem of large amounts of labeled data required for model training.

2. Natural Language Processing

Natural language processing (NLP) is a branch of artificial intelligence that processes natural human language prompt text ( any language), voice, etc. It includes sentence translation, image or video subtitles, emotion detection, and many other applications. Multi-task learning is widely used in NLP problems to improve the performance of the main task through auxiliary tasks.

3. Recommendation system

Personalized recommendations have become the main technology to help users process massive online content. To improve user experience, recommendation models must accurately predict users’ personal preferences for items.

An example of a multi-task recommendation system is the CAML model, which improves the accuracy and interpretability of explainable recommendations by tightly coupling recommendation tasks and explanation tasks.

4. Reinforcement Learning

Reinforcement learning is a paradigm of deep learning, between supervised learning and unsupervised learning. In this learning scheme, the algorithm learns by making decisions through trial and error, with correct decisions being rewarded and incorrect decisions being punished. It is commonly used in robotic applications.

Because many reinforcement learning problems do not necessarily involve complex perception, such as using text or pixels, the architectural requirements of many such problems are not high. Therefore, many deep networks used for reinforcement learning are simple fully connected, convolutional or recurrent architectures. However, in multi-task situations, information between tasks can be exploited to create improved architectures for reinforcement learning.

Like the CARE model, a hybrid encoder is used to encode input observations into multiple representations, corresponding to different skills or objects. The learning agent is then allowed to use context to decide which representation it uses for any given task, giving the agent fine-grained control over what information is shared across tasks, mitigating the negative transfer problem.

5. Multi-modal learning

As the name suggests, multi-modal learning involves multiple data modalities, such as audio, image, video, Train models on natural text, etc. These modalities may or may not be relevant. Multi-task learning is widely used to implicitly inject multi-modal features into a single model.

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