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 3551–3575 of 4925 papers

TitleStatusHype
Multilinear Hyperplane Hashing—0
Multi-modality Deep Restoration of Extremely Compressed Face Videos—0
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks—0
Multiple yield curve modelling with CBI processes—0
Multi-Prize Lottery Ticket Hypothesis: Finding Generalizable and Efficient Binary Subnetworks in a Randomly Weighted Neural Network—0
Multi-rate adaptive transform coding for video compression—0
Multirate Neural Image Compression with Adaptive Lattice Vector Quantization—0
Multiresolution Signal Processing of Financial Market Objects—0
Multi-Sample Training for Neural Image Compression—0
Multiscale Augmented Normalizing Flows for Image Compression—0
Multiscale Quantization for Fast Similarity Search—0
Multi-Scale Vector Quantization with Reconstruction Trees—0
Multi-target regression via output space quantization—0
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS—0
Multi-user Downlink Beamforming using Uplink Downlink Duality with 1-bit Converters for Flat Fading Channels—0
Multi-user Downlink Beamforming using Uplink Downlink Duality with CEQs for Frequency Selective Channels—0
Multiuser-MIMO Systems Using Comparator Network-Aided Receivers With 1-Bit Quantization—0
Self-supervised Remote Sensing Images Change Detection at Pixel-level—0
Muon-Accelerated Attention Distillation for Real-Time Edge Synthesis via Optimized Latent Diffusion—0
Music Source Separation in the Waveform Domain—0
Mutual Quantization for Cross-Modal Search With Noisy Labels—0
MuZero with Self-competition for Rate Control in VP9 Video Compression—0
MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization—0
MWQ: Multiscale Wavelet Quantized Neural Networks—0
Nanometer Scanning with Micrometer Sensing: Beating Quantization Constraints in Lissajous Trajectory Tracking—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