Generative Feature Distillation for Label-Free Vision Pre-Training
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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, distilling features from generative models into vision backbones without labels, utilizing unlabeled data for pre-training and aligning feature dimensions through neural network blocks and loss determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional supervised and unsupervised pre-training methods use object-centric datasets, then the pre-training can be performed with structured data, but the dataset lacks diversity and flexibility leading to poor training efficiency
Solution Approach 1:
The patent uses generative models to synthesize training data that copies the essential characteristics of real-world data distributions. Instead of relying on curated object-centric datasets, the system generates synthetic images and features that preserve statistical properties and relationships, enabling diverse and flexible pre-training without manual curation
Solution Approach 2:
The patent transforms the pre-training approach by changing the parameters of data generation from fixed object-centric categories to continuous feature spaces. By using generative models with adjustable parameters, the system can dynamically control data diversity, distribution, and characteristics to match specific downstream task requirements
2Quantity of substance
If third-party service providers curate large-scale object-centric datasets, then the datasets can be assembled at scale, but the providers are unaware of the AI model objectives leading to irrelevant data
Solution Approach 1:
The patent performs preliminary action by pre-training the generative model on large-scale unlabeled data before the actual training task. This preliminary pre-training enables the generative model to learn general data distributions and relationships in advance, ensuring that subsequently generated training data is inherently relevant to the downstream task without requiring third-party curation
Solution Approach 2:
The generative model serves as an intermediary between unlabeled data and the downstream task. It transforms generic unlabeled data into task-relevant training samples by learning the underlying data distribution and generating synthetic data that bridges the gap between raw data and task-specific requirements
3Ease of operation
If feature distillation is used to distill generative features to target backbones, then pre-training can be performed without labels, but the feature dimension alignment requires additional processing
Solution Approach 1:
The patent segments the feature distillation process into distinct stages: generating features from the generative model, extracting relevant feature representations, and aligning feature dimensions through separate projection layers. This segmentation allows each component to be optimized independently, reducing overall complexity while maintaining label-free operation
Solution Approach 2:
The patent introduces intermediary projection layers and feature transformation modules that mediate between the generative model's feature space and the target backbone's expected input format. These intermediaries handle dimension alignment and feature compatibility automatically, simplifying the integration process despite the complexity of cross-dimensional feature mapping
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.