Classifier Retraining Boosts Long-Tailed Recognition Accuracy.
Key takeaways
- Long-tailed recognition is a common challenge in real-world datasets.
- Retraining only the classifier after Balanced Softmax training significantly boosts few-shot accuracy.
- The BS-cRT method is a simple yet effective two-stage approach.
- It offers substantial performance gains on underrepresented classes across various benchmarks.
Who benefits
Summary
This paper introduces BS-cRT, a two-stage method for long-tailed recognition that significantly improves few-shot accuracy by retraining only the classifier after initial Balanced Softmax training. The approach consistently outperforms existing baselines across various image recognition datasets.
Why it matters
Professionals working with real-world datasets often encounter class imbalance. This method offers a straightforward and effective way to improve model performance on underrepresented classes, which is crucial for applications like fraud detection, medical diagnosis, or product recommendation where rare events are critical.
How to implement this in your domain
- 1Apply Balanced Softmax as the initial training strategy for image classification models on imbalanced datasets.
- 2Implement a second training stage where the backbone is frozen, and only the classifier weights are updated using balanced episodic batches.
- 3Evaluate the few-shot accuracy improvements on tail classes using relevant metrics for your specific application.
- 4Consider integrating this two-stage approach into existing model training pipelines for long-tailed recognition tasks.
Original post by Juan Terven, Diana Margarita C\'ordova Esparza, Julio Alejandro Romero Gonzalez, Edgar Arturo Ch\'avez Urbiola, Francisco Javier Willars Rodriguez, Juan Bautista Hurtado Ramos, Alfonso Ramirez Pedraza
"arXiv:2607.09832v1 Announce Type: new Abstract: Long-tailed recognition methods often modify losses, margins, or representations to reduce the dominance of frequent classes. We ask whether, after Balanced Softmax training, the remaining tail error can be reduced by retraining onl…"
View on XOriginally posted by Juan Terven, Diana Margarita C\'ordova Esparza, Julio Alejandro Romero Gonzalez, Edgar Arturo Ch\'avez Urbiola, Francisco Javier Willars Rodriguez, Juan Bautista Hurtado Ramos, Alfonso Ramirez Pedraza on X · view source
Want to go deeper?
Turn these trends into skills with Learnijoy's hands-on AI & tech courses.
Explore coursesMore in AI Research
Emotional Preferences Regulate Goal Priorities in Reinforcement Learning Agents
This paper proposes a computational framework where higher-level goals autonomously generate state-dependent emotional preferences to regulate the priorities of competing lower-level objectives in reinforcement learning agents. It demonstrates how this emergent preference function exhibits contextual priority switching and improves performance over fixed-preference strategies in multi-objective exploration environments.
New Framework Unifies Task Detection and Adaptation for Continual Learning
This paper proposes FiUni, a Fisher-guided unified framework for task-free continual learning in LLMs that combines batch-level task detection with parameter-efficient adaptation. FiUni uses Fisher information matrix (FIM) properties to dynamically determine whether to reuse, expand, or create new low-rank adaptation (LoRA) subspaces, effectively mitigating catastrophic forgetting without explicit task boundaries.
Soft EMG Interface Enables Machine Learning-Powered Silent Speech Recognition
This paper introduces a soft, active electromyography (EMG) interface worn on the hand that enables word-level silent speech recognition (SSR) using machine learning. The device acquires stable EMG signals from a fingertip electrode near the lips, achieving 97.2% accuracy on a 30-word vocabulary and demonstrating real-time drone control in noisy environments.