Quantization
1046 papers with code • 10 benchmarks • 18 datasets
Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).
Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Benchmarks
These leaderboards are used to track progress in Quantization
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Libraries
Use these libraries to find Quantization models and implementationsDatasets
CIFAR-10
ImageNet
MS COCO
LFW
Reddit
IJB-C
IJB-B
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison
Tiny Images
Visual Wake Words
Latest papers with no code
Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels
Our results show that there is an interesting interplay between source distortion (i. e., distortion for the probability vector measured via f-divergence) and the subsequent channel encoding/decoding parameters; and indicate that a joint design of these parameters is crucial to navigate the latency-distortion tradeoff.
AdaQAT: Adaptive Bit-Width Quantization-Aware Training
Compared to other methods that are generally designed to be run on a pretrained network, AdaQAT works well in both training from scratch and fine-tuning scenarios. Initial results on the CIFAR-10 and ImageNet datasets using ResNet20 and ResNet18 models, respectively, indicate that our method is competitive with other state-of-the-art mixed-precision quantization approaches.
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization
In federated learning, particularly in cross-device scenarios, secure aggregation has recently gained popularity as it effectively defends against inference attacks by malicious aggregators.
HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates.
EdgeFusion: On-Device Text-to-Image Generation
The intensive computational burden of Stable Diffusion (SD) for text-to-image generation poses a significant hurdle for its practical application.
Privacy-Preserving UCB Decision Process Verification via zk-SNARKs
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge.
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory
Transformer models have been successful in various sequence processing tasks, but the self-attention mechanism's computational cost limits its practicality for long sequences.
Neural Network Approach for Non-Markovian Dissipative Dynamics of Many-Body Open Quantum Systems
Simulating the dynamics of open quantum systems coupled to non-Markovian environments remains an outstanding challenge due to exponentially scaling computational costs.
QGen: On the Ability to Generalize in Quantization Aware Training
In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance.
Comprehensive Survey of Model Compression and Speed up for Vision Transformers
Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks.
