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Found 5 papers, 1 papers with code
Efficient Pruning of Large Language Model with Adaptive Estimation Fusion
Large language models (LLMs) have become crucial for many generative downstream tasks, leading to an inevitable trend and significant challenge to deploy them efficiently on resource-constrained devices.
InstructGIE: Towards Generalizable Image Editing
In response to this challenge, our study introduces a novel image editing framework with enhanced generalization robustness by boosting in-context learning capability and unifying language instruction.
DiffClass: Diffusion-Based Class Incremental Learning
On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden access to previous task data.
EdgeQAT: Entropy and Distribution Guided Quantization-Aware Training for the Acceleration of Lightweight LLMs on the Edge
In this paper, we propose EdgeQAT, the Entropy and Distribution Guided QAT for the optimization of lightweight LLMs to achieve inference acceleration on Edge devices.
Pruning Parameterization With Bi-Level Optimization for Efficient Semantic Segmentation on the Edge
With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications.
