增量学习

人工智能（AI）正在以前所未有的速度改变我们的世界，从智能手机的个性化推荐到自动驾驶汽车，AI的身影无处不在。然而，你是否曾想过，这些看似“聪明”的AI系统是如何学习和成长的？它们能否像人类一样，在学习新知识的同时不忘记旧知识，并不断地丰富自己的认知？答案是：这正是“增量学习”（Incremental Learning）试图解决的核心问题。

引言：永不停止的学习者——增量学习是什么？

想象一下我们人类的学习过程。一个孩子不会一次性学会世界上所有的知识，而是循序渐进地学习。他们先认识苹果，再认识香蕉，然后是更多水果，甚至在几年后学车、学编程，但他们并不会因此忘记苹果和香蕉长什么样。这种“边学边记，逐步丰富”的能力，正是人类智能的精髓。

然而，传统的AI模型，尤其是深度学习模型，在学习方式上与人类大相径庭。它们通常采用“批量学习”的方式：收集所有需要学习的数据，然后一次性进行训练，从零开始构建一个模型。这种方式在数据固定且充足时表现出色，但一旦出现新数据或新任务，问题就来了。如果不对模型进行重新训练，它就无法识别新信息；如果重新训练，则需要投入大量的计算资源和时间，更糟糕的是，模型可能会“忘记”之前学到的旧知识，这在AI领域被称为“灾难性遗忘”（Catastrophic Forgetting）。

“增量学习”，有时也被称为“持续学习”（Continual Learning）或“终身学习”（Lifelong Learning），正是为了解决这一痛点而生。它旨在让AI模型能够像人类一样，在获得新的训练样本后，不需抛弃已有模型进行重新训练，只对已有模型进行少量更新，就能从中吸取新知识，同时有效保留已学到的旧知识。

日常比喻：知识的“打补丁”和“更新菜单”

为了更好地理解增量学习，我们可以用几个日常生活中的概念来类比：

  1. 打补丁更新软件： 你的手机操作系统或者常用App，通常会定期收到更新。这些更新不是让你每次都卸载旧版本，再从头安装一个新版本，而是在现有系统的基础上，打上一些“补丁”，增加新功能或修复bug。增量学习就像是给AI模型打补丁，让它在原有知识的基础上，悄无声息地吸收新知识，而不是每次都“重装系统”。
  2. 厨师的新菜谱： 想象一位经验丰富的厨师，他掌握了数千道菜肴的做法。如果他想学习一道新菜，他不会把之前所有的菜谱都扔掉，然后从头开始学习烹饪。相反，他会把新菜谱加入到自己的知识库中，并融会贯通，在保持原有菜品水准的同时，拓宽自己的“菜谱”。增量学习就是这样，AI模型学习新知识，就像厨师学会一道新菜，它是在现有“菜谱”上做加法，而不是推倒重来。
  3. 图书馆的新书入库： 一座图书馆每隔一段时间就会有新的藏书入库。图书馆管理员不会因此而销毁所有旧书，重新规划整个图书馆的布局和索引。他们只会把新书分门别类地归档，更新索引系统，让读者能够同时找到新书和旧书。增量学习就是这样一个持续更新和整合的过程，让AI的知识库不断壮大。

核心原理：边学边记，而非推倒重来

增量学习的核心魅力在于，它允许模型在吸收新信息时，不会完全忘记过去所学。这听起来简单，但在技术实现上却充满了挑战，其中最大的障碍就是前面提到的“灾难性遗忘”。 当模型用新数据训练时，它为了适应新数据的特征，可能会大幅调整内部参数，结果导致对旧数据的识别能力急剧下降。

为了对抗“灾难性遗忘”，研究者们提出了多种策略：

  • 记忆回放（Memory Replay）： 这就像人类在学习新知识时，会时不时温习一下旧知识。增量学习模型会保留少量的旧数据样本（或者这些样本的特征），在学习新数据时，混合旧样本进行训练。 这样可以帮助模型“回忆”起以前学到的东西，从而巩固旧知识，同时学习新知识。
  • 正则化（Regularization）： 这种方法的核心思想是“保护”那些对旧知识至关重要的模型参数。在学习新任务时，算法会施加约束，避免对这些关键参数进行过大的调整。这就像是给模型的某些“记忆区域”加上了保护锁，让它们不容易被新信息擦除。
  • 知识蒸馏（Knowledge Distillation）： 当有新任务到来时，先用旧模型对新数据进行预测，得到一个“软目标”。新模型在学习新数据的同时，也要尽量模仿旧模型在历史数据上的输出，从而间接保留旧知识。

为什么增量学习如此重要？

增量学习不仅在模仿人类学习方式上具有吸引力，它更承载着AI走向更智能、更实用未来的希望。其重要性体现在多个方面：

  • 数据效率与资源节约： 传统的批量学习需要大量数据进行一次性训练，且新数据到来时需要重新训练。增量学习则允许模型逐步吸收新数据，无需保留所有历史数据，大大减少了存储空间和计算资源。
  • 适应动态环境： 现实世界是不断变化的，新的物体、新的语言模式、新的用户偏好层出不穷。增量学习使AI系统能够实时适应这些变化，无需频繁地离线重新部署。
  • 隐私保护： 在许多应用场景（如医疗、金融）中，数据的隐私性至关重要，大量数据不允许集中存储和训练。增量学习允许模型在本地学习新数据，只需要偶尔传输更新模型的少量信息，从而更好地保护用户隐私。
  • 迈向真正的通用人工智能（AGI）： 终身学习是通用人工智能的关键特征之一，AI只有具备了像人类一样持续学习和适应的能力，才能真正实现跨领域、跨任务的智能化。

它解决了哪些实际问题？

增量学习的应用场景广泛，特别是在数据持续生成、环境不断变化的领域：

  • 自动驾驶： 自动驾驶汽车需要不断学习识别新的路况、交通标志、行人行为等。增量学习可以帮助车辆的AI系统在行驶过程中不断更新其对世界的认知，而不必每次都从头学习。
  • 机器人： 服务型机器人或工业机器人可能需要在新的环境中执行新任务，识别新物体，增量学习使其能够快速适应并扩展技能。
  • 推荐系统： 用户的兴趣和商品趋势每天都在变化。增量学习能让推荐系统实时更新用户的偏好模型，提供更精准的个性化推荐。
  • 智能客服与对话AI： 随着新产品和新问题的出现，客服机器人需要不断学习新的问答知识和对话模式，增量学习确保它们能持续提供优质服务。
  • 金融风控与网络安全： 欺诈手段和网络攻击模式不断演变，金融风控和网络安全系统需要快速学习并识别新的威胁，增量学习能帮助它们及时调整预测模型。
  • 医疗诊断： 随着新的疾病和诊断技术不断出现，医疗AI系统如果能利用增量学习，就能持续提升诊断准确性和效率。

最新的进展与挑战

近年来，随着深度学习的飞速发展，增量学习也取得了显著进步。研究者们在算法层面不断创新，例如提出了基于元学习的增量学习算法，通过使模型在多个任务之间共享知识，以减少灾难性遗忘的发生。 此外，增量学习在无监督学习和迁移学习中的应用也展现出巨大的潜力，为模型的持续适应提供了新思路。

然而，增量学习仍然面临着诸多挑战：

  • 灾难性遗忘的有效缓解： 尽管已有多种方法，但完全消除灾难性遗忘仍然是一个难题。如何在学习新知识的同时，完美保留所有旧知识，是研究仍在攻克的方向。 例如，Meta FAIR在2025年10月提出了一种稀疏记忆微调法，尝试通过仅更新与新知识高度相关且在预训练中少用的记忆槽，来实现高效学习新事实同时大幅缓解灾难性遗忘，但其本质仍属于记忆增强，距离真正意义上的技能持续学习尚有距离。
  • 新旧知识的平衡： 在增量学习中，新类别的数据通常比旧类别更丰富，如何平衡新旧类别数据的学习，避免模型在新类别上过度拟合而损害旧类别的性能，是一个重要的研究方向。
  • 可解释性： 相比传统学习方法，增量学习模型内部的知识更新机制更为复杂，其决策过程的可解释性仍有待提升。
  • 大规模模型的持续学习： 对于参数量巨大的大语言模型（LLMs）等大规模预训练模型，如何进行高效、低成本的增量学习，是当前研究的热点和难点。 工业界也开始探索针对大模型持续学习的混合架构方法来解决灾难性遗忘问题。

展望未来：迈向真正的“终身学习”AI

增量学习是AI领域一个充满活力的研究方向，它致力于让AI具备像人类一样的“终身学习”能力。尽管挑战重重，但它代表了AI发展的一个重要趋势：从静态、孤立的“一次性学习”迈向动态、持续的“永不停止学习”。 随着算法的不断演进和计算能力的提升，我们有理由相信，未来的AI系统将不再是只会“背书”的“学霸”，而是能够快速适应、自我成长、真正融入我们生活每一个角落的“智能伙伴”。 想象一个AI，它能陪你从童年到老年，持续学习你的习惯，理解社会的变化，不断进步，那将是一个多么令人期待的未来。

Introduction: The Never-Stopping Learner — What is Incremental Learning?

Imagine our human learning process. A child does not learn all the knowledge in the world at once, but learns step by step. They first recognize apples, then bananas, then more fruits, and even learn to drive and program a few years later, but they do not forget what apples and bananas look like because of this. This ability to “learn and remember, gradually enrich” is the essence of human intelligence.

However, traditional AI models, especially deep learning models, are very different from humans in their learning methods. They usually adopt “batch learning”: collecting all the data needed for learning, and then training all at once to build a model from scratch. This method works well when the data is fixed and sufficient, but once new data or new tasks appear, problems arise. If the model is not retrained, it cannot recognize new information; if it is retrained, it requires a lot of computing resources and time. Even worse, the model may “forget” the old knowledge learned before, which is called “Catastrophic Forgetting” in the AI field.

“Incremental Learning,” sometimes also called “Continual Learning” or “Lifelong Learning,” was born to solve this pain point. It aims to enable AI models to learn new knowledge from new training samples without discarding the existing model for retraining, just like humans, by only making small updates to the existing model, while effectively retaining the learned old knowledge.

Daily Metaphors: “Patching” Knowledge and “Updating Menus”

To better understand incremental learning, we can use a few concepts from daily life as analogies:

  1. Patching Software Updates: Your mobile operating system or commonly used apps usually receive regular updates. These updates do not require you to uninstall the old version and install a new version from scratch every time, but apply some “patches” on the basis of the existing system to add new functions or fix bugs. Incremental learning is like patching an AI model, allowing it to absorb new knowledge quietly on the basis of original knowledge, rather than “reinstalling the system” every time.
  2. Chef’s New Recipe: Imagine an experienced chef who has mastered the cooking methods of thousands of dishes. If he wants to learn a new dish, he will not throw away all previous recipes and start learning cooking from scratch. Instead, he will add the new recipe to his knowledge base and integrate it, broadening his “menu” while maintaining the standard of original dishes. Incremental learning is like this. An AI model learning new knowledge is like a chef learning a new dish. It is adding to the existing “menu” rather than starting over.
  3. New Books in the Library: A library will have new books in stock every once in a while. Librarians will not destroy all old books and re-plan the layout and index of the entire library because of this. They will only classify and archive new books and update the index system so that readers can find both new and old books. Incremental learning is such a continuous update and integration process, allowing AI’s knowledge base to grow continuously.

Core Principle: Learn and Remember, Not Start Over

The core charm of incremental learning lies in that it allows the model to absorb new information without completely forgetting what it has learned in the past. This sounds simple, but it is full of challenges in technical implementation, the biggest obstacle being the “Catastrophic Forgetting” mentioned earlier. When a model is trained with new data, in order to adapt to the characteristics of the new data, it may significantly adjust internal parameters, resulting in a sharp decline in the ability to recognize old data.

To combat “Catastrophic Forgetting,” researchers have proposed various strategies:

  • Memory Replay: This is like humans reviewing old knowledge from time to time when learning new knowledge. Incremental learning models will retain a small number of old data samples (or features of these samples) and mix old samples for training when learning new data. This helps the model “recall” what it learned before, thereby consolidating old knowledge while learning new knowledge.
  • Regularization: The core idea of this method is to “protect” model parameters that are crucial to old knowledge. When learning new tasks, the algorithm imposes constraints to avoid excessive adjustments to these key parameters. This is like putting a protective lock on certain “memory areas” of the model so that they are not easily erased by new information.
  • Knowledge Distillation: When a new task arrives, first use the old model to predict the new data to get a “soft target.” While learning new data, the new model should also try to imitate the output of the old model on historical data, thereby indirectly retaining old knowledge.

Why is Incremental Learning So Important?

Incremental learning is not only attractive in mimicking human learning methods, but also carries the hope of AI moving towards a smarter and more practical future. Its importance is reflected in several aspects:

  • Data Efficiency and Resource Saving: Traditional batch learning requires a large amount of data for one-time training, and retraining is required when new data arrives. Incremental learning allows the model to absorb new data gradually without retaining all historical data, greatly reducing storage space and computing resources.
  • Adapting to Dynamic Environments: The real world is constantly changing, with new objects, new language patterns, and new user preferences emerging one after another. Incremental learning enables AI systems to adapt to these changes in real-time without frequent offline redeployment.
  • Privacy Protection: In many application scenarios (such as medical care, finance), data privacy is crucial, and large amounts of data are not allowed to be stored and trained centrally. Incremental learning allows models to learn new data locally, only needing to transmit a small amount of information to update the model occasionally, thereby better protecting user privacy.
  • Moving Towards True Artificial General Intelligence (AGI): Lifelong learning is one of the key features of Artificial General Intelligence. Only when AI has the ability to learn and adapt continuously like humans can it truly achieve cross-domain and cross-task intelligence.

What Practical Problems Does It Solve?

Incremental learning has a wide range of application scenarios, especially in fields where data is continuously generated and the environment is constantly changing:

  • Autonomous Driving: Autonomous vehicles need to constantly learn to recognize new road conditions, traffic signs, pedestrian behaviors, etc. Incremental learning can help the vehicle’s AI system constantly update its cognition of the world during driving without having to learn from scratch every time.
  • Robotics: Service robots or industrial robots may need to perform new tasks and recognize new objects in new environments. Incremental learning enables them to quickly adapt and expand skills.
  • Recommendation Systems: User interests and product trends change every day. Incremental learning allows recommendation systems to update user preference models in real-time and provide more accurate personalized recommendations.
  • Intelligent Customer Service and Conversational AI: With the emergence of new products and new problems, customer service robots need to constantly learn new Q&A knowledge and dialogue patterns. Incremental learning ensures that they can continue to provide high-quality services.
  • Financial Risk Control and Network Security: Fraud methods and cyber attack patterns are constantly evolving. Financial risk control and network security systems need to quickly learn and identify new threats. Incremental learning can help them adjust prediction models in time.
  • Medical Diagnosis: With the continuous emergence of new diseases and diagnostic technologies, if medical AI systems can use incremental learning, they can continuously improve diagnostic accuracy and efficiency.

Latest Progress and Challenges

In recent years, with the rapid development of deep learning, incremental learning has also made significant progress. Researchers continue to innovate at the algorithm level, for example, proposing incremental learning algorithms based on meta-learning, which reduce the occurrence of catastrophic forgetting by enabling models to share knowledge across multiple tasks. In addition, the application of incremental learning in unsupervised learning and transfer learning has also shown great potential, providing new ideas for the continuous adaptation of models.

However, incremental learning still faces many challenges:

  • Effective Mitigation of Catastrophic Forgetting: Although there are various methods, completely eliminating catastrophic forgetting is still a difficult problem. How to perfectly retain all old knowledge while learning new knowledge is a direction that research is still tackling. For example, Meta FAIR proposed a sparse memory fine-tuning method in October 2025, attempting to achieve efficient learning of new facts while significantly mitigating catastrophic forgetting by only updating memory slots that are highly relevant to new knowledge and rarely used in pre-training, but its essence still belongs to memory enhancement, and there is still a distance from true continuous skill learning.
  • Balance of New and Old Knowledge: In incremental learning, data of new categories is usually richer than that of old categories. How to balance the learning of new and old category data to avoid the model overfitting on new categories and damaging the performance of old categories is an important research direction.
  • Interpretability: Compared with traditional learning methods, the knowledge update mechanism inside incremental learning models is more complex, and the interpretability of its decision-making process still needs to be improved.
  • Continuous Learning of Large-scale Models: For large-scale pre-trained models such as Large Language Models (LLMs) with huge parameters, how to perform efficient and low-cost incremental learning is a hot spot and difficulty in current research. The industry has also begun to explore hybrid architecture methods for continuous learning of large models to solve the problem of catastrophic forgetting.

Looking to the Future: Moving Towards True “Lifelong Learning” AI

Incremental learning is a vibrant research direction in the AI field, dedicated to enabling AI to have “lifelong learning” capabilities like humans. Despite the challenges, it represents an important trend in AI development: moving from static, isolated “one-time learning” to dynamic, continuous “never-stopping learning.” With the continuous evolution of algorithms and the improvement of computing power, we have reason to believe that future AI systems will no longer be “top students” who only know how to “memorize books,” but “intelligent partners” who can adapt quickly, grow themselves, and truly integrate into every corner of our lives. Imagine an AI that can accompany you from childhood to old age, continuously learn your habits, understand social changes, and constantly improve. That will be a future worth looking forward to.