增量学习——Maintaining Discrimination and Fairness in Class Incremental Learning——CVPR2020

本文探讨了在增量学习中使用知识蒸馏保持分类器的判别力,并通过一种称为权重对齐的方法来维持公平性,以解决最后一层全连接层权重在类增量学习中高度偏置的问题。该研究基于《大规模增量学习》和《通过再平衡学习统一分类器》两篇文章,采用复习策略,聚焦于一个具体问题并取得了良好的实验结果。

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Abstract

knowledge distillation; 造成灾难性遗忘的很大一个原因是the weights in the last fully connected layer are highly biased in class-incremental learning;

Introduction

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Conclusion

maintain the discrimination via knowledge distillation and maintains the fairness via a method called weight aligning

Key points: code开源;这篇文章的思路是基于《large scale incremental learning,CVPR2019》和《Learning a unified classifier via rebalancing》做的;实验性文章;也是基于rehearsal strategy;找到一个切入点做一个工作,做出好的实验结果

### YOLO Incremental Learning Object Detection Implementation and Methods Incremental learning aims to enable models like YOLO to learn new classes without forgetting previously learned information while adapting efficiently with minimal data. Traditional deep convolutional neural networks require extensive offline training on large annotated datasets, which limits their ability to adapt incrementally or online [^2]. However, recent advancements have explored ways to integrate incremental learning into object detectors such as YOLO. #### Challenges in Implementing Incremental Learning for YOLO The primary challenge lies in preventing catastrophic forgetting when introducing new categories. This phenomenon occurs because updating model parameters based solely on newly introduced samples can overwrite knowledge about earlier seen objects. To address this issue, several strategies are employed: - **Knowledge Distillation**: A technique where an older version of the network acts as a teacher guiding the newer iteration during training by providing soft targets alongside ground truth labels. - **Feature Replay Mechanisms**: Storing past feature representations allows maintaining historical context even after encountering novel instances. Techniques include storing actual images from previous tasks or generating synthetic ones through generative adversarial networks (GANs). - **Class-Incremental Loss Functions**: Designing loss functions that penalize significant changes between old and updated weights helps maintain stability across task transitions. ```python import torch.nn.functional as F def class_incremental_loss(old_logits, current_logits): """Computes distillation loss.""" T = 2.0 # Temperature scaling factor return F.kl_div( F.log_softmax(current_logits / T, dim=1), F.softmax(old_logits / T, dim=1), reduction='batchmean' ) * (T * T) # Example usage within training loop for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() output = yolo_model(data) ce_loss = criterion(output, target) if epoch > start_of_new_classes: distilled_loss = class_incremental_loss(stored_old_outputs[data_indices], output) total_loss = ce_loss + lambda_distill * distilled_loss else: total_loss = ce_loss total_loss.backward() optimizer.step() ``` Incorporating these mechanisms ensures better retention of pre-existing skills while acquiring proficiency over fresh concepts sequentially presented throughout deployment phases. For specific implementations tailored towards YOLO architectures, researchers often modify components responsible for bounding box regression along with classification heads according to chosen methodologies mentioned above. Additionally, fine-tuning only certain layers instead of retraining entire structures proves beneficial under resource-constrained environments typical among edge devices requiring real-time processing capabilities. --related questions-- 1. How does knowledge distillation contribute to mitigating catastrophic forgetting? 2. What role do GAN-based replay buffers play in preserving long-term memory within continual learning systems? 3. Can you explain how temperature scaling affects performance metrics in cross-task transfer scenarios involving CNN-based classifiers? 4. Which parts of the YOLO architecture typically undergo adjustments specifically designed for supporting continuous learning paradigms?
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