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 976–1000 of 4925 papers

TitleStatusHype
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model CompressionCode0
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise ModelingCode0
NITRO-D: Native Integer-only Training of Deep Convolutional Neural NetworksCode0
Make RepVGG Greater Again: A Quantization-aware ApproachCode0
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural NetworksCode0
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling MatricesCode0
Convert, compress, correct: Three steps toward communication-efficient DNN trainingCode0
Compressed Object DetectionCode0
Low-Precision Stochastic Gradient Langevin DynamicsCode0
Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based RefinementCode0
Compressed 3D Gaussian Splatting for Accelerated Novel View SynthesisCode0
LSQ++: Lower running time and higher recall in multi-codebook quantizationCode0
Low-bit Model Quantization for Deep Neural Networks: A SurveyCode0
Convolutional Neural Networks to Enhance Coded SpeechCode0
CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUsCode0
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-TuningCode0
Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message PropagationCode0
Comprehensive SNN Compression Using ADMM Optimization and Activity RegularizationCode0
Low-bit Quantization of Neural Networks for Efficient InferenceCode0
Comprehensive Comparisons of Uniform Quantization in Deep Image CompressionCode0
Comprehensive Analysis of the Object Detection Pipeline on UAVsCode0
Bit Error Robustness for Energy-Efficient DNN AcceleratorsCode0
On Quantizing Neural Representation for Variable-Rate Video CodingCode0
Compositional Sketch SearchCode0
Composite QuantizationCode0
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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