Generative Pre-Training for Vision Backbone Feature Distillation
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Solution Overview
Problem
Conventional supervised and unsupervised pre-training methods for image-based and video-based AI rely on object-centric datasets that lack diversity and flexibility, leading to poor training efficiency and increased costs, especially when curated by third-party providers unaware of the AI model's objectives.
Innovation Solution
Employ unsupervised or semi-supervised pre-training using generative models and large-scale unlabeled data to improve accuracy in downstream tasks, leveraging feature distillation and label distillation to align and update the vision backbone without labeled datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If object-centric datasets are used for pre-training, then the pre-training process can be completed with structured data, but the diversity and flexibility of the training data is limited
Solution Approach 1:
A domain adaptation layer is introduced as an intermediary component between the pre-trained backbone and the downstream task. This layer enables the model to adapt to different domains and tasks without requiring retraining of the entire backbone, thereby increasing data flexibility and diversity while maintaining the structured pre-training approach
Solution Approach 2:
The model is segmented into two independent parts: a frozen pre-trained backbone and a trainable domain adaptation layer. This segmentation allows the backbone to be trained on structured object-centric data while the adaptation layer learns from diverse, domain-specific data, resolving the contradiction between structured training completeness and data diversity
2Ease of operation
If third-party curated datasets are used, then data assembly is simplified, but the relevance to specific AI model objectives is reduced
Solution Approach 1:
The domain adaptation layer serves as a mediator that bridges generic third-party curated data and task-specific requirements. It allows the model to use easily assembled third-party datasets while adapting them to be relevant for specific AI model objectives through task-specific training of the adaptation layer
Solution Approach 2:
The backbone is pre-trained in advance on general object-centric data from third-party sources. This preliminary action simplifies data assembly while the subsequent domain adaptation phase ensures relevance to specific model objectives by fine-tuning the adaptation layer on task-specific data
3Loss of information
If conventional supervised pre-training is used, then labeled datasets can be utilized, but training costs and time increase
Solution Approach 1:
The backbone is pre-trained once on large-scale labeled object-centric datasets. This preliminary supervised pre-training captures general visual features efficiently. Subsequently, the domain adaptation layer is trained on smaller task-specific datasets, reducing the overall training time and cost while still utilizing labeled data effectively
Solution Approach 2:
Instead of training the entire model with labeled data, only the domain adaptation layer is trained with task-specific labeled data while the backbone features are copied from the pre-trained model. This reduces training costs and time while preserving the benefits of labeled data utilization
4Measurement precision
If the entire model is retrained for downstream tasks, then task-specific accuracy can be optimized, but training efficiency decreases
Solution Approach 1:
The model is segmented into a frozen backbone and a trainable domain adaptation layer. Only the adaptation layer is retrained for downstream tasks, maintaining task-specific accuracy optimization while significantly improving training efficiency by avoiding retraining of the entire model
Solution Approach 2:
The task-specific adaptation capability is extracted from the entire model and isolated into a separate domain adaptation layer. This allows the backbone to remain frozen while the adaptation layer is optimized for specific tasks, achieving task accuracy without the computational cost of full model retraining
Data Source
AI summary
In various examples, systems and methods are disclosed relating to generating a response from image and/or video input for image/video-based artificial intelligence (AI) systems and applications. Systems and methods are disclosed for a first model (e.g., a teacher model) distilling its knowledge to a second model (a student model). The second model receives a downstream image in a downstream task and generates at least one feature. The first model generates first features corresponding to an image which can be a real image or a synthetic image. The second model generates second features using the image as an input to the second model. Loss with respect to first features is determined. The second model is updated using the loss.