SOTAVerified

Quantization

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1575 of 4925 papers

TitleStatusHype
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian SplattingCode3
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding—0
Chronos: Learning the Language of Time SeriesCode7
Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems—0
COMQ: A Backpropagation-Free Algorithm for Post-Training QuantizationCode1
QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning—0
FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization—0
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation—0
FrameQuant: Flexible Low-Bit Quantization for TransformersCode1
Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision—0
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers—0
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLMCode2
Enhancing Multimodal Unified Representations for Cross Modal Generalization—0
Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN InferenceCode0
Self-Adapting Large Visual-Language Models to Edge Devices across Visual ModalitiesCode1
QAQ: Quality Adaptive Quantization for LLM KV CacheCode2
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks—0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
ShortGPT: Layers in Large Language Models are More Redundant Than You ExpectCode2
Adaptive Integrate-and-Fire Time Encoding Machine with Quantization—0
Design of Stochastic Quantizers for Privacy Preservation—0
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs—0
Behavior Generation with Latent ActionsCode3
Deep-Learned Compression for Radio-Frequency Signal Classification—0
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion ModelsCode3
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified