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A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …
Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Dinov2: Learning robust visual features without supervision
The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …
quantities of data have opened the way for similar foundation models in computer vision …
[HTML][HTML] Accurate medium-range global weather forecasting with 3D neural networks
Weather forecasting is important for science and society. At present, the most accurate
forecast system is the numerical weather prediction (NWP) method, which represents …
forecast system is the numerical weather prediction (NWP) method, which represents …
SpectralGPT: Spectral remote sensing foundation model
The foundation model has recently garnered significant attention due to its potential to
revolutionize the field of visual representation learning in a self-supervised manner. While …
revolutionize the field of visual representation learning in a self-supervised manner. While …
Diffusion policy: Visuomotor policy learning via action diffusion
This paper introduces Diffusion Policy, a new way of generating robot behavior by
representing a robot's visuomotor policy as a conditional denoising diffusion process. We …
representing a robot's visuomotor policy as a conditional denoising diffusion process. We …
Videomae v2: Scaling video masked autoencoders with dual masking
Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …
generalize to a variety of downstream tasks. However, it is still challenging to train video …
Eyes wide shut? exploring the visual shortcomings of multimodal llms
Is vision good enough for language? Recent advancements in multimodal models primarily
stem from the powerful reasoning abilities of large language models (LLMs). However the …
stem from the powerful reasoning abilities of large language models (LLMs). However the …
Vision-language models for vision tasks: A survey
Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …
(DNNs) training, and they usually train a DNN for each single visual recognition task …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …