Low-Rank Adaptation

MMD-LoRA: Integrating LoRA and Contrastive Learning for Depth Estimation

by

🫱Click here to join the 16 specialized direction discussion group (🔥Recommended)🫲 Abstract: The authors introduce a Multi-Modality Driven Low-Rank Adaptation (MMD-LoRA) method that utilizes low-rank adaptation matrices to achieve efficient fine-tuning from the source domain to the target domain, addressing the Adverse Condition Depth Estimation (ACDE) problem. It consists of two core components: Prompt-based Domain … Read more

LoRA: Low-Rank Adaptation for Large Models

by
LoRA: Low-Rank Adaptation for Large Models

Source: DeepHub IMBA This article is approximately 1000 words and is recommended to be read in 5 minutes. Low-Rank Adaptation significantly reduces the number of trainable parameters for downstream tasks. For large models, it becomes impractical to fine-tune all model parameters. For example, GPT-3 has 175 billion parameters, making both fine-tuning and model deployment impossible. … Read more