Model distillation is a new deep learning technique that aims to transfer knowledge from complex models to small models. This technology is widely used in various fields, such as natural language processing, computer vision and speech recognition.
We invited Mr. H, a doctor from the Chinese Academy of Sciences and winner of multiple international computer competitions to bring us——Small model, big wisdom: Model distillation helps optimize AI algorithm performance, explaining in detail the unlimited potential of distillation models in all directions.
Participate in the course for free (free PPT taught by the teacher)
A collection of 100+ deep learning and distillation model papers
(There are benefits at the end of the article)
Instructor introduction: Teacher H
-Graduated from the Chinese Academy of Sciences
-Research direction: multimedia retrieval, focusing on cross-modal retrieval between text and video
-Published multiple top conference papers as the first author, including CVPR, WACV, etc.
-Participated in multiple international competitions, and as the first participant won the top three in 4 international competitions and the fourth place in 1 international competition. He participated in the 2021 CVPR international competition EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge, and finally won the international first place. 1st place result
Live broadcast outline
1. Unified distillation model in autonomous driving
1. Distillation technology in autonomous driving
2. Cross-modal unified distillation framework from the BEV perspective
3. Future development direction of distillation model for autonomous driving
2. Multi-modal distillation model
1. Current status of research on multimodal distillation models
2. An advanced multi-modal distillation model framework
3. Future development direction of multi-modal distillation technology
The application of model distillation in various fields is constantly expanding. In the future, we expect to see more research exploring how to better utilize distillation methods for knowledge transfer to improve model performance and efficiency. From September 5th to September 6th, Teacher H will spend 3-4 hours explaining the distillation model technology in detail from two major directions.
Some time ago,Google researchers and researchers from the University of Washington proposed a newmodel distillation method: Detailed explanation of the step-by-step distillation method, namely the Distilling step-by-step method, which can be applied to all types of language models without limitations! This method is very valuable and will be of great help in miniaturizing models in the future. It has attracted a lot of discussions on Twitter.
Get a collection of 100+ deep learning and distillation model papers for free
(There are benefits at the end of the article)
1. In the field of natural language processing
Distillation methods are widely used for compression and acceleration of language models. For example, documents such as Distillation with Soft Targets and Knowledge Distillation with Gaussian Teachers propose new distillation methods to improve the knowledge transfer effect of the model. Additionally, some studies have explored the use of distillation methods for cross-language and cross-domain model transfer.
2. In the field of computer vision
Distillation methods are also widely used for model compression and acceleration. For example, some studies transfer the knowledge of teacher models to small network models to improve the accuracy of image classification and object detection. Additionally, some studies have explored the use of distillation methods for cross-domain and cross-modal data migration.
3. In the field of speech recognition
Distillation methods are also widely used for model compression and acceleration. For example, some studies transfer the knowledge of teacher models into small network models to improve the accuracy of speech recognition and speech conversion. Additionally, some studies have explored the use of distillation methods for cross-domain and cross-modal data migration.
In an era when all the people are actively turning inward (volume), there are often rumors in the world:With SCI in hand, you will get a promotion and salary increase, and a worry-free future.
The threshold for publishing a paper is either high or low. Regardless of whether you are a second-year or third-year graduate student with rich scientific research experience or a novice graduate student, you can publish a paper. Sometimes what limits you from publishing a paper is not your writing skills or experiments, but the initial but most important idea.
For students who want to publish a paper, are interested in scientific research, or are preparing for scientific research, two points are crucial if they want to publish a paper quickly.
1.紧跟风口.
The easiest way to publish an article is to keep up with the trend and follow the research trends in the field to determine your own direction. After all, in popular fields, there are far more innovations and ideas than in other non-popular fields.
2. Have an experienced and capable senior to guide you.
Everyone is working hard to design new networks, new strategies, and new training algorithms. As long as we can achieve a good performance on a certain problem, the paper will come naturally. And if you want to achieve it quickly, guidance from seniors is indispensable.
Therefore, the problem to be solved is:
1. Find the outlet
2. Find a master in the field as a mentor
Scan QR code
One-on-one meeting with Daniel mentor
Participate in the course for free (free PPT taught by the teacher)
Get a collection of 100+ deep learning and distillation model papers for free
(There are benefits at the end of the article)
Welfare at the end of the article
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