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Found 4 papers, 3 papers with code
Shortened LLaMA: A Simple Depth Pruning for Large Language Models
Structured pruning of modern large language models (LLMs) has emerged as a way of decreasing their high computational needs.
BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
Text-to-image (T2I) generation with Stable Diffusion models (SDMs) involves high computing demands due to billion-scale parameters.
DreamBooth Personalized Generation
Image-to-Image Translation
Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy
As an attempt to solve this problem, pruning filters is a common solution, but most existing pruning methods do not preserve the model accuracy efficiently and therefore require a large number of finetuning epochs.
SuperLoss: A Generic Loss for Robust Curriculum Learning
The key idea is to somehow estimate the importance (or weight) of each sample directly during training based on the observation that easy and hard samples behave differently and can therefore be separated.
