Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Abstract We present AiTLAS: Benchmark Arena–an open-source benchmark suite for
evaluating state-of-the-art deep learning approaches for image classification in Earth …

We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …

The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

Scaling up neural networks has led to remarkable performance across a wide range of
tasks. Moreover, performance often follows reliable scaling laws as a function of training set …

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of
training on large amounts of noisy image-text data, without relying on expensive accurate …

The current modus operandi in adapting pre-trained models involves updating all the
backbone parameters, ie., full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) …

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-
tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers …