Since then, deep neural networks (DNN) have achieved great success in both industry and academia, especially in computer vision tasks. The success of deep learning is largely due to its scalable architecture for encoding data with billions of parameters. Its training goal is to model the relationship between inputs and outputs on an existing training data set. Performance is highly dependent on the complexity of the network and the amount and quality of labeled training data.
Compared with traditional algorithms in the field of computer vision, most DNN-based models have strong generalization capabilities due to over-parameterization. This kind of generalization The ability to model is reflected in the fact that the model can give better prediction results for all data input to a certain problem, whether it is training data, test data, or unknown data belonging to the problem.
In the current context of deep learning, in order to improve the prediction effect of business algorithms, algorithm engineers often have two solutions:
Use parameterized more complex networks. This type of network The learning ability is very strong, but it requires a lot of computing resources to train, and the inference speed is slow.
Integrated model integrates many models with weaker effects, usually including the integration of parameters and the integration of results.
These two solutions can significantly improve the effect of existing algorithms, but they both increase the scale of the model, generate a greater computational burden, and require large computing and storage resources.
In work, the ultimate purpose of various algorithm models is to serve a certain application. Just like in buying and selling we need to control our income and expenses. In industrial applications, in addition to requiring models to have good predictions, the use of computing resources must also be strictly controlled, and we cannot only consider results without considering efficiency. In the field of computer vision where the amount of input data encoding is high, computing resources are even more limited, and the resource occupancy of the control algorithm is even more important.
Generally speaking, larger models have better prediction results, but the long training time and slow inference speed make it difficult to deploy the model in real time. Especially on devices with limited computing resources, such as video surveillance, self-driving cars, and high-throughput cloud environments, the response speed is obviously not enough. Although smaller models have faster inference speed, due to insufficient parameters, the inference effect and generalization performance may not be as good. How to weigh large-scale models and small-scale models has always been a hot topic. Most of the current solutions are to select a DNN model of appropriate size based on the performance of the terminal device in the deployment environment.
If we want to have a smaller model that can achieve the same or close effect as the large model while maintaining a fast inference speed, how can we do it?
In machine learning, we often assume that there is a potential mapping function relationship between input and output. Learning a new model from scratch is an approximately unknown mapping function between the input data and the corresponding label. On the premise that the input data remains unchanged, training a small model from scratch is difficult to approach the effect of a large model from an empirical point of view. In order to improve the performance of small model algorithms, generally speaking, the most effective way is to label more input data, that is, to provide more supervision information, which can make the learned mapping function more robust and perform better. To give two examples, in the field of computer vision, the instance segmentation task can improve the effect of target bounding box detection by providing additional mask information; the transfer learning task can significantly improve new tasks by providing a pre-trained model on a larger data set. prediction effect. Therefore Providing more supervision information may be the key to shortening the gap between small-scale models and large-scale models.
According to the previous statement, obtaining more supervision information means labeling more training data, which often requires huge costs. So is there a low-cost and efficient method of obtaining supervision information? ? The 2006 article [1] pointed out that the new model can be made to approximate the original model (the model is a function). Because the function of the original model is known, more supervision information is naturally added when training the new model, which is obviously more feasible.
Thinking further, the supervision information brought by the original model may contain different dimensions of knowledge. These unique information may not be captured by the new model itself. To some extent, this is important for the new model. The model is also a kind of "cross-domain" learning.
In 2015, Hinton followed the idea of approximation in the paper "Distilling the Knowledge in a Neural Network" [2] and took the lead in proposing the concept of "Knowledge Distillation (Knowledge Distillation, KD)": you can first train By building a large and powerful model and then transferring the knowledge contained in it to a small model, the purpose of "maintaining the fast reasoning speed of the small model while achieving an effect equivalent to or close to that of the large model" is achieved. The large model trained first can be called the teacher model, and the small model trained later is called the student model. The entire training process can be vividly compared to "teacher-student learning." In the following years, a large amount of knowledge distillation and teacher-student learning work emerged, providing the industry with more new solutions. Currently, KD has been widely used in two different fields: model compression and knowledge transfer [3].
Knowledge Distillation is a method based on "teacher-student The model compression method based on the "network" idea is widely used in the industry because of its simplicity and effectiveness. Its purpose is to extract the knowledge contained in the trained large model - Distill (Distill) - into another small model. So how to transfer the knowledge or generalization ability of the large model to the small model? The KD paper provides the probability vector of the sample output of the large model to the small model as a soft target, so that the output of the small model can be as close to this soft target as possible (originally it is to one-hot encoding) to approximate the learning of large models. model behavior.
In the traditional hard label training process, all negative labels are treated uniformly, but this method separates the relationship between categories. For example, if you want to recognize handwritten digits, some pictures with the same label as "3" may look more like "8", and some may look more like "2". Hard labels cannot distinguish this information, but a well-trained large model can give out. The output of the softmax layer of the large model, in addition to positive examples, negative labels also contain a lot of information. For example, the probability corresponding to some negative labels is much greater than other negative labels. The behavior of approximate learning allows each sample to bring more information to the student network than traditional training methods.
Therefore, the author modified the loss function when training the student network, allowing the small model to fit the probability distribution of the large model output while fitting the ground truth labels of the training data. This method is called Knowledge Distillation Training, KD Training. The training samples used in the knowledge distillation process can be the same as those used to train the large model, or an independent Transfer set can be found.
Detailed explanation of method
During training, the knowledge learned by the teacher model is first distilled to the small model. Specifically, for sample X, the penultimate layer of the large model is first divided by a temperature T, and then a soft target is predicted through softmax. The same goes for the model. The penultimate layer is divided by the same temperature T, and then predicts a result through softmax, and then uses the cross entropy of this result and the soft target as part of the total loss of training. Then the cross entropy of the normal output of the small model and the true value label (hard target) is used as another part of the total loss of training. Total loss weights the two losses together as the final loss for training the small model.
When the small model is trained and needs to be predicted, there is no need for the temperature T anymore, and it can be output directly according to the conventional softmax.
The FitNet paper introduces intermediate-level hints during distillation to guide the training of student models. Use a wide and shallow teacher model to train a narrow and deep student model. When performing hint guidance, it is proposed to use a layer to match the output shapes of the hint layer and the guided layer. This is often called an adaptation layer in the work of future generations.
In general, it is equivalent to using not only the logit output of the teacher model, but also the middle layer feature map of the teacher model as supervision information when doing knowledge distillation. What can be imagined is that it is too difficult for small models to directly imitate large models at the output end (the deeper the model, the more difficult it is to train, and it is quite tiring to transmit the last layer of supervision signals to the front). It is better to add some supervision signals in the middle, so that the model can learn more difficult mapping functions layer by layer during training, instead of directly learning the most difficult mapping function; in addition, hint guidance accelerates the convergence of the student model. Finding better local minima on a non-convex problem allows the student network to be deeper and train faster. It feels like our purpose is to let students do college entrance examination questions, so we first teach them the junior high school questions (first let the small model use the first half of the model to learn to extract the underlying features of the image), and then return to the original purpose , to learn college entrance examination questions (use KD to adjust all parameters of the small model).
This article is the ancestor of the proposed distillation intermediate feature map. The algorithm proposed is very simple, but the idea is groundbreaking.
The specific method of FitNets is:
In actual training, it is often used in conjunction with KD Training in the previous section, using a two-stage method for training: first use hint training to pretrain the parameters of the first half of the small model, and then use KD Training to train the entire parameter. Since more supervision information is used in the distillation process, the distillation method based on the intermediate feature map performs better than the distillation method based on the result logits, but the training time is longer.
Knowledge distillation is very effective for transferring knowledge from an ensemble or from a highly regularized large model to a smaller model. Distillation works very well even when data for any one or more classes is missing from the migration dataset used to train the distillation model. After the classics KD and FitNet were proposed, a variety of distillation methods sprung up. In the future, we also hope to make further exploration in the fields of model compression and knowledge transfer.
Ma Jialiang is a senior computer vision algorithm engineer at NetEase Yidun. He is mainly responsible for the research, development, optimization and innovation of computer vision algorithms in the field of content security.
The above is the detailed content of Large models accurately feed back small models, and knowledge distillation helps improve AI algorithm performance.. For more information, please follow other related articles on the PHP Chinese website!
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