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Found 5 papers, 3 papers with code
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different devices.
Synergistic Self-supervised and Quantization Learning
In this paper, we propose a method called synergistic self-supervised and quantization learning (SSQL) to pretrain quantization-friendly self-supervised models facilitating downstream deployment.
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments.
Ranked #1 on
Quantization
on ImageNet
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond
Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to their saving of computation resources and reproducibility of results.
Yes-Net: An effective Detector Based on Global Information
This paper introduces a new real-time object detection approach named Yes-Net.
