Catastrophic forgetting: the amnesia of machine learning and how to avoid it

Machine learning has undeniably made remarkable strides in recent years, enabling algorithms to learn from data and make predictions with unprecedented accuracy.

However, there’s a critical problem that continues to plague the field: catastrophic forgetting, also known as catastrophic interference.

This phenomenon occurs when machine learning models forget previously learned information as they acquire new knowledge, leading to suboptimal performance and even complete failure.

In this post, we’ll explore what catastrophic forgetting is, how it happens, and why it poses such a challenge for machine learning researchers today.

What is catastrophic forgetting?

Catastrophic forgetting is a phenomenon whereby machines learning from data sets forget what they have previously learned when faced with new data.

This can be a major problem in machine learning, as it can prevent machines from being able to learn from new data sets and improve their performance over time.

There are a number of possible causes of catastrophic forgetting, but the most likely cause is simply that the machine’s learning algorithm is not able to effectively deal with the increased complexity of the new data set.

Catastrophic forgetting can be a serious problem for machine learning applications, but there are a number of methods that can be used to mitigate its effects.

Why is it a problem for machine learning?

There are a few reasons why catastrophic forgetting is such a problem for machine learning.

First, it can lead to inaccurate models. If a model forgets important information, it may no longer be able to accurately predict outcomes.

Second, catastrophic forgetting can also lead to increased training time and cost. If a model has to relearn forgotten information, it will take longer to train and may require more data.

Finally, catastrophic forgetting can also reduce the overall performance of a model. If a model forgets important information, it may no longer be able to generalize well to new data.

What causes catastrophic forgetting?

Catastrophic forgetting is a problem that occurs when training a machine learning model on a new task, causing the model to forget how to perform the old task.

This happens because the model is overwritten with new information when learning the new task.

Catastrophic forgetting can be prevented by using a technique called transfer learning, which allows the model to keep the old information while learniung new information.

How can catastrophic forgetting be avoided?

Catastrophic forgetting is a major problem for machine learning, as it can cause a model to forget critical information that it has learned. There are a few ways to avoid this problem:

Regularization

Regularization involves adding a penalty term to the model’s loss function to prevent overfitting to the new data.

By doing so, the model is encouraged to retain information learned from the previous data while adapting to the new data.

There are different types of regularization techniques, such as L1, L2 regularization, and dropout.

L1 regularization

L1 regularization adds a penalty proportional to the absolute value of the weights. This technique tends to result in sparse weights where only a few of them are significantly different from zero.

L2 regularization

L2 regularization, on the other hand, adds a penalty proportional to the square of the weights. This technique results in all weights being decreased, but not reduced to zero.

Dropout

Dropout is another regularization technique that randomly drops out some neurons during training to avoid overfitting.

Rehearsal

Rehearsal involves retaining a portion of the previous dataset and training the model on both the old and new data. This technique is also known as incremental learning or lifelong learning.

By training on both the old and new data, the model can retain information learned from the previous data while adapting to the new data.

One way to implement rehearsal is to use a buffer, such as a memory replay buffer, to store a subset of previous examples and use them to train the model alongside new data.

Parameter freezing

Parameter freezing involves freezing a portion of the model’s weights during training to prevent them from being updated. This technique ensures that the previous knowledge learned by the model is retained and not overwritten by new data.

One way to implement parameter freezing is to divide the model into two parts: the old part, which contains the frozen weights, and the new part, which contains the trainable weights.

The old part is used to process the previous data, while the new part is trained on the new data. By doing so, the model can learn the new information while retaining the previous knowledge.

In summary, regularization, rehearsal, and parameter freezing are effective techniques used to mitigate catastrophic forgetting in machine learning models.

By using these techniques, models can learn new information while retaining the knowledge learned from previous data.

Example scenarios of catastrophic forgetting

There are many real world situations where catastrophic forgetting may occur:

Image classification

An image classification model trained on a specific dataset, such as MNIST (handwritten digits), can forget previously learned information when retrained on a new dataset, such as CIFAR-10 (color images of objects).

The model can become too specialized on the new data, causing it to forget how to recognize the handwritten digits it previously learned.

Language models

Language models, such as recurrent neural networks (RNNs) and transformers, can also suffer from catastrophic forgetting.

For example, a language model trained on news articles can forget previously learned information when trained on tweets.

The model can become too focused on the language used in tweets, causing it to forget how to generate coherent sentences.

Robotics

Robotics is another field where catastrophic forgetting can occur. For example, a robot trained to perform a specific task can forget how to perform that task when retrained on a new task.

This can occur because the robot becomes too specialized on the new task and forgets how to perform the previous task.

Reinforcement learning

In reinforcement learning, a model can forget previously learned policies when trained on new environments or tasks.

For example, a robot that has learned to navigate a specific environment can forget how to navigate that environment when placed in a new environment.

Conclusion

Catastrophic forgetting can be a challenging issue to overcome in machine learning, but there are various methods that can help mitigate its effects.

With the right techniques and algorithms, we can make sure our ML models don’t forget what they have learned and continue to improve over time without being affected by past data.