Investigating Catastrophic Forgetting of Deep Learning Models ...

Deep learning models have shown impressive performance in various tasks. However, they are prone to a phenomenon called catastrophic forgetting.

[2312.10549] Catastrophic Forgetting in Deep Learning - arXiv

Deep Learning models have achieved remarkable performance in tasks such as image classification or generation, often surpassing human accuracy.

Catastrophic Forgetting in Deep Learning: A Comprehensive ... - arXiv

Incremental learning without forgetting is widely recognized as a crucial aspect in building better AI systems, as it allows models to adapt to ...

Investigating Catastrophic Forgetting During Continual Training for ...

Neural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget ...

Generalisable deep Learning framework to overcome catastrophic ...

Lastly, the issue is that DL models can suffer catastrophic forgetting when trained on new data and lose the ability to recall old data. Using feature fusion ...

Investigating the Catastrophic Forgetting in Multimodal Large...

Following the success of GPT4, there has been a surge in interest in multimodal large language model (MLLM) research.

Catastrophic Forgetting in Deep Learning - Journals

Abstract. Deep Learning models have achieved remarkable performance in tasks such as image classification or generation, often surpassing human ...

Overcoming catastrophic forgetting in neural networks - PNAS

Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, ...

Mitigating Catastrophic Forgetting in Deep Learning Models for ...

A study is conducted to investigate the impact of catastrophic forgetting on sentiment analysis algorithms and propose potential solutions to address this issue ...

Investigating the Catastrophic Forgetting in Multimodal Large ...

language model for few-shot learning. Advances in ... cal investigation of catastrophic forgetting in gradient-based neural networks ... terminal phase of deep ...

Understanding and Mitigating Catastrophic Forgetting in Machine ...

In the ever-evolving landscape of artificial intelligence and machine learning, the ability of models to adapt to new information is crucial ...

Overcoming 'Catastrophic Forgetting': A Leap in AI Continuous ...

Summary: Researchers are investigating a significant hurdle in machine learning known as “catastrophic forgetting,” a phenomenon where AI ...

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming ...

In both scenarios, we present the model with some new data and test it for its knowledge retention about past data. To investigate the potential of historical ...

Forget the Catastrophic Forgetting - Communications of the ACM

Their study analyzed a simple linear machine learning model and quantified how much knowledge from old tasks was forgotten when it learned new ...

EnnengYang/Awesome-Forgetting-in-Deep-Learning - GitHub

Harmful forgetting occurs when we desire the machine learning model to retain previously learned knowledge while adapting to new tasks, domains, or environments ...

Investigating the Catastrophic Forgetting in Multimodal Large ...

Investigating the Catastrophic Forgetting in Multimodal Large Language Model Fine-TuningYuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Q...

Forgetting in Deep Learning - Towards Data Science

Neural network models suffer from the phenomenon of catastrophic forgetting: a model can drastically lose its generalization ability on a task ...

An Empirical Investigation of Catastrophic Forgetting in ... - NASA ADS

Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, ...

Reducing Catastrophic Forgetting With Associative Learning

Catastrophic forgetting, when neural networks inadvertently overwrite old memories with new memories, remains a long-standing problem in machine ...

An Empirical Investigation of Catastrophic Forgeting in Gradient ...

PDF | Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second.