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 3126–3150 of 4925 papers

TitleStatusHype
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method—0
Non-asymptotic spectral bounds on the -entropy of kernel classes—0
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Characterizing and Understanding the Behavior of Quantized Models for Reliable DeploymentCode0
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis—0
Bimodal Distributed Binarized Neural NetworksCode0
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data—0
Scaling Language Model Size in Cross-Device Federated Learning—0
Ternary and Binary Quantization for Improved Classification—0
Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Autoregressive Co-Training for Learning Discrete Speech RepresentationsCode0
Reverse Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC—0
REx: Data-Free Residual Quantization Error Expansion—0
New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor—0
SPIQ: Data-Free Per-Channel Static Input Quantization—0
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization—0
MKQ-BERT: Quantized BERT with 4-bits Weights and Activations—0
Fast on-line signature recognition based on VQ with time modeling—0
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models—0
FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation—0
Compression of Generative Pre-trained Language Models via Quantization—0
Symbol quantization in interstellar communications: methods and observations—0
Image Storage on Synthetic DNA Using Autoencoders—0
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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