ENTERPRISE AI ANALYSIS
ReLKD: Inter-Class Relation Learning with Knowledge Distillation for Generalized Category Discovery
The paper introduces ReLKD, an end-to-end framework for Generalized Category Discovery (GCD). GCD addresses the challenge of classifying unlabeled data with both known and novel classes, given only labels for known classes. ReLKD improves novel class discovery by exploiting implicit inter-class relations and hierarchical structures, transferring knowledge across different levels of the class hierarchy. It comprises a target-grained module, a coarse-grained module, and a distillation module. Experiments on four datasets demonstrate ReLKD's effectiveness, especially with limited labeled data.
Quantifiable Impact
Our analysis reveals key performance indicators for implementing ReLKD: Inter-Class Relation Learning with Knowledge Distillation for Generalized Category Discovery within your enterprise.
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Novel Class ACC (Improvement)
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Overall Classification ACC (CIFAR-100)
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Overall Classification ACC (ImageNet-100)
Deep Analysis & Enterprise Applications
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Generalized Category Discovery (GCD) aims to classify instances from an unlabeled set into novel classes, given labels for known classes. This research highlights the challenge of limited supervisory signals for unknown classes and the need for methods that can discover new categories while recognizing known ones. ReLKD addresses this by leveraging inter-class relations and hierarchical information.
Knowledge Distillation (KD) improves student model performance by guiding it to mimic a teacher. ReLKD uses a novel form of KD to transfer class correlations from a coarse-grained level to a target-grained level, enhancing target-level classification performance. This contrasts with traditional KD methods that typically assume teacher and student models are trained for the same task and class sets.
Real-world data often exhibits inherent hierarchical relations and semantic similarities between categories. ReLKD is the first framework to explicitly introduce hierarchical structure information into GCD. It learns implicit hierarchical relations by generating pseudo-labels for coarse-grained categories and transferring this knowledge to refine target-grained representation learning.
84.5%
ReLKD Novel Class ACC (CIFAR-100) with substantial improvement
Enterprise Process Flow
Target-Grained Module (Discriminative Reps)
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Coarse-Grained Module (Hierarchical Relations)
→
Distillation Module (Knowledge Transfer)
→
Enhanced Novel Class Discovery
| Feature | SimGCD | ReLKD |
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| Inter-Class Relations |
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| Supervision for Novel Classes |
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| Architecture |
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Impact of Hierarchical Knowledge on Real-World Datasets
In a challenging scenario like the Aircraft dataset, where coarse-grained categories are not explicitly defined, ReLKD still achieved substantial improvements. This demonstrates its ability to learn and leverage implicit hierarchical structures even without explicit supervision.
On the Scars dataset, focusing on finer-grained image categorization, ReLKD reached state-of-the-art results. This highlights the framework's robustness and effectiveness in scenarios with high intra-class similarity and complex inter-class structures.
The most significant gains were observed in Novel Class Discovery metrics across all datasets, validating the core hypothesis that inter-class relation learning is crucial for identifying previously unseen categories effectively.
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Implementation Timeline
Our structured approach ensures a seamless integration and rapid value realization for your enterprise.
Phase 1: Data Integration & Initial Training
Integrate your existing labeled and unlabeled datasets. Initialize the ReLKD framework with pre-trained models and begin target-grained representation learning.
Phase 2: Coarse-Grained Relation Learning
Activate the coarse-grained module to learn implicit hierarchical relations and generate pseudo-super-class labels, refining inter-class similarities.
Phase 3: Knowledge Distillation & Fine-Tuning
Implement the knowledge distillation module to transfer relational insights from coarse to target-grained levels, enhancing novel class discovery.
Phase 4: Validation & Deployment
Validate the model's performance on unseen novel classes and integrate the ReLKD system into your existing classification pipelines for real-world application.
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