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 701–725 of 4925 papers

TitleStatusHype
EasyQuant: Post-training Quantization via Scale OptimizationCode1
Lightweight Diffusion Models for Resource-Constrained Semantic CommunicationCode1
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense RetrievalCode1
Catastrophic Failure of LLM Unlearning via QuantizationCode1
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical StudyCode1
Automatic Perturbation Analysis for Scalable Certified Robustness and BeyondCode1
Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike FluctuationsCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial LearningCode1
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and QuantizationCode1
Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital FossaCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
AlignQ: Alignment Quantization With ADMM-Based Correlation PreservationCode1
Lossy Compression with Gaussian DiffusionCode1
Distributed Learning Systems with First-order MethodsCode1
LQER: Low-Rank Quantization Error Reconstruction for LLMsCode1
BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionCode1
LSQ+: Improving low-bit quantization through learnable offsets and better initializationCode1
Adaptive Debanding FilterCode1
Making DensePose fast and lightCode1
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense EmbeddingsCode1
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution NetworksCode1
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and CompressionCode1
Disentanglement via Latent QuantizationCode1
Show:102550
← PrevPage 29 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