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 876–900 of 4925 papers

TitleStatusHype
Learning Statistical Texture for Semantic SegmentationCode1
Learning to Groove with Inverse Sequence TransformationsCode1
Learning to Improve Image Compression without Changing the Standard DecoderCode1
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep LearningCode1
Compressing LLMs: The Truth is Rarely Pure and Never SimpleCode1
Lexico: Extreme KV Cache Compression via Sparse Coding over Universal DictionariesCode1
Conditional Coding and Variable Bitrate for Practical Learned Video CodingCode1
ConveRT: Efficient and Accurate Conversational Representations from TransformersCode1
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual LearningCode1
Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric QuantizerCode1
NIPQ: Noise proxy-based Integrated Pseudo-QuantizationCode1
Transferable Sparse Adversarial AttackCode1
Lightweight Super-Resolution Head for Human Pose EstimationCode1
Self-Adapting Large Visual-Language Models to Edge Devices across Visual ModalitiesCode1
Model-Aware Deep Architectures for One-Bit Compressive Variational AutoencodingCode0
Model Compression Techniques in Biometrics Applications: A SurveyCode0
Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor DecompositionCode0
A Tale of Two Models: Constructing Evasive Attacks on Edge ModelsCode0
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic ProgrammingCode0
Model compression via distillation and quantizationCode0
Mixed Non-linear Quantization for Vision TransformersCode0
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the EdgeCode0
Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMsCode0
Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation RegularizationCode0
Show:102550
← PrevPage 36 of 197Next →
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