Joint Representation Learning with Pseudo-Labels for Unknown Categories
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Solution Overview
Problem
Deep learning models require large amounts of human-annotated training data and struggle to classify unlabeled data with new categories not represented in the labeled data, making them costly and unsuitable for real-world environments.
Innovation Solution
A flexible end-to-end framework using unified contrastive learning and Winner-Take-All hashing to enable deep learning models to learn feature representations and perform cluster assignments from a mixture of labeled and unlabeled data, employing instance and category discrimination with a unified loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained with huge amounts of human-annotated training data to outperform humans on selected tasks, then model performance is improved, but training cost and time increase significantly
Solution Approach 1:
The model performs preliminary learning on unlabeled data from new categories before formal classification is required. By pre-processing unlabeled data through contrastive learning and generating pseudo-labels in advance, the system prepares the feature representations ahead of time, so when actual classification is needed, the model can quickly adapt without requiring extensive retraining on labeled data.
Solution Approach 2:
The system uses itself to generate training signals by creating pseudo-labels for unlabeled data through contrastive learning and hashing mechanisms. The model learns to generate its own training data by identifying similarities between unlabeled instances and labeled instances, effectively serving its own training needs without external human annotation for every data point.
2Measurement precision
If deep learning models are configured to recognize similarities between labeled and unlabeled training examples, then classification of unlabeled data with preexisting categories is improved, but capability to classify with new categories deteriorates
Solution Approach 1:
The system segments the classification task into two distinct pathways: one for preexisting categories using traditional supervised learning, and another for new categories using contrastive learning with pseudo-labels. This segmentation allows the model to handle known categories with high accuracy while simultaneously developing adaptability to new categories through separate learning mechanisms that don't rely on pre-labeled data.
Solution Approach 2:
Pseudo-labels serve as an intermediary between unlabeled data and the classification system. The hashing mechanism and contrastive learning generate these intermediate labels that bridge the gap between unlabeled instances and category definitions, enabling the model to gradually adapt to new categories without requiring direct human annotation for each new class.
3Measurement precision
If models use traditional supervised learning on labeled data, then classification accuracy is improved, but ability to handle unlabeled data from new categories deteriorates
Solution Approach 1:
The system merges supervised learning on labeled data with unsupervised contrastive learning on unlabeled data into a unified framework. By combining these two learning paradigms, the model simultaneously maintains high classification accuracy on known categories through supervised signals while developing the ability to handle unlabeled new category data through contrastive objectives that learn meaningful representations without requiring labels.
Data Source
AI summary
Systems and methods for providing deep learning models capable of performing joint representation learning and new category discovery on a mixture of labeled and unlabeled data, which may include single- and multi-modal data. In some examples, a flexible end-to-end framework uses unified contrastive learning on labeled and unlabeled data based on both instance discrimination and category discrimination, and further uses Winner-Take-All hashing to generate a pseudo-label based on the similarity between each pair of unlabeled data points that can be used to train the model to generate clustering assignments for each unlabeled data point. In some examples, the unified contrastive learning may be further based on cross-modal discrimination.