Encoder Training via Alternating Self-Supervised Phases
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current supervised feature learning methods in computer vision require large amounts of manually annotated data and are prone to overfitting when using self-supervised pre-training, limiting their effectiveness in tasks like object detection and segmentation.
Innovation Solution
A method that interleaves self-supervised and supervised learning phases to train an encoder and perception component using unannotated and annotated sensor data sets, avoiding overfitting by updating encoder parameters based on both self-supervised and task-specific losses during alternating training steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If self-supervised pre-training is used to reduce annotation requirements, then the need for manual annotation is reduced, but the model becomes prone to overfitting
Solution Approach 1:
The training process alternates between self-supervised learning phases (using unannotated data to update encoder parameters) and supervised learning phases (using annotated data to update perception component parameters). This periodic switching prevents overfitting by regularly incorporating task-specific supervision while reducing the overall annotation burden through extended periods of self-supervised training.
Solution Approach 2:
The training process is divided into distinct phases: self-supervised training phases that update encoder parameters using unannotated data, and supervised training phases that update perception component parameters using annotated data. This segmentation allows each component to be trained appropriately with the right type of data, preventing overfitting while reducing annotation requirements.
2Measurement precision
If large amounts of annotated training data are used to achieve state-of-the-art performance, then perception task accuracy is improved, but the annotation burden and training cost increase significantly
Solution Approach 1:
The encoder is pre-trained using self-supervised learning on large amounts of unannotated data before the supervised fine-tuning phase. This preliminary action allows the model to learn robust feature representations from abundant unannotated data, reducing the amount of expensive annotated data needed to achieve high perception task accuracy.
Solution Approach 2:
The encoder is trained to perform multiple functions: first, it learns general feature representations through self-supervised learning on unannotated data, and then it is fine-tuned for specific perception tasks using annotated data. This multi-functionality allows the same encoder to benefit from both unannotated and annotated data, achieving high accuracy without requiring exclusively large amounts of annotated data.
3Quantity of substance
If the encoder is trained only on unannotated data through self-supervised learning, then annotation requirements are reduced, but the model lacks task-specific feature optimization
Solution Approach 1:
The training alternates between self-supervised phases (optimizing for general feature extraction from unannotated data) and supervised phases (optimizing for task-specific features using annotated data). This periodic action ensures the encoder maintains both general feature extraction capabilities and task-specific optimization, achieving adaptability while reducing overall annotation requirements.
Data Source
AI summary
An encoder is trained together with a perception component based on a training set comprising unannotated sensor data sets and annotated sensor data sets in a sequence of multiple training steps. Each training step comprises: in a first phase of the training step, updating the set of encoder parameters based on the unannotated sensor data sets, with the aim of optimizing a self-supervised loss function, without updating the set of task-specific parameters of the perception component, and in a second phase of the training step, updating the set of task-specific parameters based on the annotated sensor data sets, with the aim of optimizing a task-specific loss function, wherein the encoder as updated in the first phase of that training step processes a data representation of each annotated sensor data set to extract features therefrom, wherein the perception component processes the extracted features to compute an output therefrom, and wherein the task-specific loss is defined on the output and the associated annotation for each annotated sensor data set for learning a desired perception task. In performing the sequence of multiple training steps, the method alternates repeatedly between the first phase and the second phase.