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 4851–4875 of 4925 papers

TitleStatusHype
Hybrid Scene Compression for Visual Localization—0
Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression—0
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights—0
Hyperbolic Residual Quantization: Discrete Representations for Data with Latent Hierarchies—0
HyperDepth: Learning Depth From Structured Light Without Matching—0
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine—0
Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Hyperspherical Loss-Aware Ternary Quantization—0
Hyperspherical Quantization: Toward Smaller and More Accurate Models—0
Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing—0
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space—0
ICQ: A Quantization Scheme for Best-Arm Identification Over Bit-Constrained Channels—0
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression—0
IDKM: Memory Efficient Neural Network Quantization via Implicit, Differentiable k-Means—0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models—0
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection—0
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA—0
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG—0
Image Compression using only Attention based Neural Networks—0
Image Compression with Product Quantized Masked Image Modeling—0
Image De-Quantization Using Generative Models as Priors—0
Image processing in DNA—0
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks—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