Finally I understand fine-tuning, refinement and transfer learning in deep learning!!

Today I will share with you three important knowledge points in deep learning: fine-tuning, refinement, and transfer learning

In deep learning, fine-tuning, distillation, and transfer learning are three common model optimization techniques, which are mainly used to improve the generalization ability of the model, reduce training time, and optimize resource utilization.

Fine-tuning

Fine-tuning refers to further training some or all of the parameters of an already trained model (usually a pre-trained model) to adapt to a specific new task.

Usually, the pre-trained model is trained on a large-scale dataset (such as ImageNet), which can learn some common features. Fine-tuning is based on this, by training on new tasks, further adjusting the model parameters to make it better suited to new tasks.

How it works

1. Pre-training

   First, a deep learning model is pre-trained using a large-scale dataset (such as ImageNet) to obtain the basic capabilities and common features of the model.

2. Freeze some layers (optional)

   Generally speaking, the bottom layer of the model (close to the input layer) extracts common features such as edges and textures, while the high layer (close to the output layer) extracts high-level features specific to the task. Therefore, the bottom layer weights can be frozen and only the high-level parameters can be trained.

3. Adjusting the model structure

   If the number of categories of the new task is different from that of the original task, the last fully connected layer or output layer needs to be replaced.

4. train

   When training with a new dataset, a smaller learning rate is usually used to avoid destroying the common features that have been learned.

Applicable scenarios

• Smaller data volume: Training a deep learning model from scratch requires a large amount of data, while fine-tuning can leverage existing knowledge and reduce data requirements.
• High task similarity: If the new task is similar to the pre-training task (such as cat and dog classification vs. animal classification), fine-tuning can adapt quickly.

advantage

• The training speed is fast because only some parameters need to be fine-tuned to avoid training from scratch.
• The knowledge from large-scale datasets can be used to improve the performance of the model on small datasets.

Refinement (knowledge distillation)

Knowledge distillation is a model compression technique that transfers the knowledge of a large and complex model (usually called the teacher model) into a smaller and simpler model (called the student model).

Through refinement (knowledge distillation), the student model can learn the behavior and prediction patterns of the teacher model and achieve similar results while maintaining a smaller model size and faster inference speed.

How it works

1. Teacher model training

   First, a large and complex teacher model is trained.

2. Generate soft labels

   The teacher model infers the training data and produces soft labels, which are the model's predicted probability for each category.

   These soft labels contain the relationship between the categories (such as 80% cat, 15% fox, 5% dog) and are more informative than hard labels (100% cat).

3. Student model training

   The student model is trained by minimizing the difference between it and the teacher model output (soft labels).

   During the training process, the student model not only learns the correct labels, but also learns the teacher model's "understanding" of the samples, so that it can better approach the performance of the teacher model.

Distillation loss

Common loss functions are:

in

• CE is the cross entropy loss, which is used to preserve the true label information.
• The KL divergence measures the difference between the prediction distributions of the student model and the teacher model.
•  Control the weight of both.

Application Scenario

• Mobile deployment

  When deep learning models need to be deployed on devices with limited computing resources (such as smartphones, embedded devices, etc.), large models can be compressed into smaller models through distillation.

• Accelerate reasoning

  Small student models are often more efficient at inference time than large teacher models and are suitable for applications that require low-latency responses.

advantage

• Reduce the consumption of computing resources and shorten the inference time of the model.
• The storage space of the model can be significantly reduced while maintaining high accuracy.

Transfer Learning

Transfer learning is a technique that uses knowledge learned in one task to apply it to another related task.

In simple terms, transfer learning uses existing knowledge to transfer from the source domain (source task) to the target domain (target task). This is often particularly useful when there is insufficient data in the target domain, and it can avoid training the model from scratch.

Types of Transfer Learning

1. Feature Migration

   Directly use the low-level features of the pre-trained model, such as CNN to extract features, and then use SVM, random forest, etc. for classification.

   Suitable for computer vision tasks, such as using ResNet as a feature extractor.

2. Parameter Migration (Fine-Tuning)

   Transfer the parameters of the pre-trained model to the new task and perform fine-tuning.

   For example, ResNet trained on ImageNet was fine-tuned on medical image classification.

3. Cross-domain migration

   Applicable to scenarios with different data distributions, such as migrating from English NLP tasks to Chinese tasks.

   Common methods include adversarial training, self-supervised learning, etc.

4. Cross-task transfer

   Let the model learn multiple tasks at the same time to improve generalization ability.

   For example, in the field of NLP, BERT can be used for both sentiment analysis and question answering tasks.

advantage

• It can effectively reduce the demand for training data in the target task, especially when the target task data is insufficient.
• Speed ​​up training and improve model performance, especially when the target task has small data volume.

Summarize

• Fine-tuning: Adapting a pre-trained model to a new task by performing small-scale training on it.
• Distillation: Optimize the efficiency and storage of the model by transferring the knowledge of the large model to the small model.
• Transfer Learning: Apply the knowledge learned from one task to another related task to solve the problem of insufficient data.

These three are often used in combination in practical applications. Choosing appropriate technology according to specific task requirements can significantly improve the effectiveness and efficiency of deep learning models.