Search Results for author:
Found 6 papers, 3 papers with code
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities. However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their practical applications.
Test-Time Adaptation with Perturbation Consistency Learning
Accordingly, in this paper, we propose perturbation consistency learning (PCL), a simple test-time adaptation method to promote the model to make stable predictions for samples with distribution shifts.
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Then a dual generator is trained by taking the output from the former generator as input.
Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers
Many well-trained Convolutional Neural Network(CNN) models have now been released online by developers for the sake of effortless reproducing.
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
In this paper, we investigate a novel deep-model reusing task.
