Investigating the Catastrophic Forgetting in Multimodal ...

Investigating the catastrophic forgetting in multimodal large language models. Y Zhai, S Tong, X Li, M Cai, Q Qu, YJ Lee, Y Ma. Conference on Parsimony and ...

Overcoming Catastrophic Forgetting in Massively Multilingual ...

(2022) study continual learning in a cross- lingual setting limited to just six languages. The cross-lingual abilities of pre-trained models ...

Investigating Catastrophic Forgetting of Deep Learning Models ...

... Multi-Domain Adaptation for. Neural Machine Translation: A Survey,” Journal of Artificial. Intelligence Research, vol. 75, 2022, doi: 10.1613 ...

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 ...

Measuring Catastrophic Forgetting in Neural Networks

In this paper, we study catastrophic forgetting in MLP-based ... igating catastrophic forgetting (in the data permutation and multi-modal experiments).

Multimodal Continual Instruction Tuning with Positive Forward Transfer

faces two major obstacles: catastrophic forgetting (where old knowledge is forgotten) and negative forward transfer (where the performance of future tasks ...

Catastrophic Forgetting in Deep Learning - Journals

More classifiers, less forgetting: A generic multi ... A comprehensive, application-oriented study of catastrophic forgetting in dnns.

‪Yuexiang Zhai‬ - ‪Google 学术搜索‬

Investigating the Catastrophic Forgetting in Multimodal Large Language Model. Y Zhai, S Tong, X Li, M Cai, Q Qu, YJ Lee, Y Ma. Conference on Parsimony and ...

Potential of Multimodal Large Language Models for Data Mining of ...

One significant challenge in fine-tuning MLLMs is catastrophic forgetting ... Investigating the catastrophic forgetting in multimodal large ...

Continual Learning and Catastrophic Forgetting

Though it is well-known that deep neural networks (DNNs) have achieved state-of-the-art performances in many machine learning (ML) tasks, the standard multi- ...

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

classes and several models, including Multi-layer Perceptron ... Bonin, “The stability-plasticity dilemma: Investigating the continuum from catastrophic ...

LoRA vs. Full Fine-Tuning: An Illusion of Equivalence | Hacker News

I wonder how this compares to 'catastrophic forgetting' that can be a problem of full fine tuning. ... investigating why fine-tune and LoRA ...

Fine-Tuning LLMs: Navigating Catastrophic Forgetting and Multi ...

In this blog post, we'll explore the concept of catastrophic forgetting and how fine-tuning LLMs for multiple tasks can help overcome this issue.

Multi-Modal Meta Continual Learning - IEEE Xplore

However, the modal differences in multi-modal distributions aggravate the catastrophic forgetting. In this paper, we augment multi-initial meta learning for ...

Measuring Catastrophic Forgetting in Visual Question Answering

We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA). Moti-.

Alleviating Catastrophic Forgetting via Multi-Objective Learning

Now let us investigate if pseudo-rehearsal is able to avoid catastrophic forgetting. According to [32], we generate 25 pseudo-patterns by creating random ...

Techniques for Tackling Catastrophic Forgetting in AI Models

Top 7 Tools for Building Multimodal AI Applications. Nov 13th 2024 ... study on the effect of catastrophic forgetting on continual learning.

Overcoming Catastrophic Forgetting by Incremental Moment Matching

An empiri- cal investigation of catastrophic forgetting in gradient-based neural networks. ... Regularized multi–task learning. In Proceed- ings of the ...

Switching for Continual Instruction Tuning of Large Language Models

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning ... Beyond Anti-Forgetting: Multimodal Continual ...

Quantifying catastrophic forgetting in continual federated learning

To the best of our knowledge, this is the first such study of episodic replay for CFL. ... multi-modal models, and modern reinforcement learning techniques, ...