Multi-Scale Encoder Reconstruction for Faster Self-Supervised Training
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Solution Overview
Problem
The existing self-supervised visual representation learning methods based on mask-reconstruction are inefficient due to reliance on a single final output feature of an encoder for image reconstruction, leading to slow learning processes.
Innovation Solution
The method employs an encoding network with multiple network blocks at different depths, each corresponding to a decoding network, using features at various scales for image reconstruction and incorporating random occlusion to generate diverse supervision information, thereby improving learning efficiency and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single final output feature of an encoder is used for image reconstruction, then the model structure is simple, but the learning efficiency is slow
Solution Approach 1:
The patent divides the encoding network into multiple network blocks at different depths, with each block corresponding to a separate decoding network. This segmentation allows multiple decoding networks to process features at different scales simultaneously, improving learning efficiency without requiring a completely new model architecture. The segmentation principle resolves the contradiction by organizing the complex multi-scale processing into modular, manageable units.
Solution Approach 2:
The patent introduces a depth dimension to the model architecture by stacking multiple network blocks at different depths, where each depth level corresponds to a different feature scale. This dimensional expansion allows the model to utilize features from multiple depth levels simultaneously, transforming the single-scale reconstruction limitation into a multi-scale processing capability that improves learning efficiency.
2Measurement precision
If only the final output feature of an encoder is used for reconstruction, then the implementation is simple, but the feature extraction detail is insufficient
Solution Approach 1:
The encoding network is segmented into multiple network blocks, each extracting features at different depths and scales. This segmentation enables the system to capture both high-level semantic information from deeper blocks and low-level detailed information from shallower blocks, thereby improving feature extraction precision while maintaining a structured and manageable architecture.
Solution Approach 2:
Different network blocks at different depths are assigned to different decoding networks, allowing each decoding network to specialize in reconstructing features at its corresponding scale. This local quality assignment ensures that detailed local features from shallow blocks and global semantic features from deep blocks are both preserved and utilized, enhancing overall feature extraction precision.
3Productivity
If multiple decoding networks are designed for network layers at different depths, then learning efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the model into multiple decoding networks, each responsible for a specific depth level of the encoding network. This segmentation enables parallel processing of features at different scales during training, improving model training efficiency by allowing simultaneous optimization of multiple feature representations rather than sequential processing.
Solution Approach 2:
Each decoding network is designed to handle features from its corresponding network block, creating a universal pattern that can be applied across all depth levels. This multi-functional architecture allows the system to process multiple feature scales through a consistent framework, improving training efficiency while keeping the complexity increase manageable through pattern repetition rather than unique custom designs for each scale.
Data Source
AI summary
A method and an apparatus for training a model, an electronic device, and a storage medium are provided. The method includes: dividing a training image into blocks, to obtain a plurality of first image blocks; performing occlusion on the plurality of first image blocks, to obtain a plurality of second image blocks; inputting a feature vector of each second image block into an encoding network to perform encoding, to obtain a plurality of encoding features corresponding to a plurality of network blocks; inputting each encoding feature into a decoding network corresponding to each encoding feature to perform image reconstruction, to obtain a reconstructed image corresponding to each decoding network; and training the model based on the reconstructed image corresponding to each decoding network and supervision information corresponding to each decoding network.