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 176–200 of 4925 papers

TitleStatusHype
LeanVec: Searching vectors faster by making them fitCode2
Bolt: Accelerated Data Mining with Fast Vector CompressionCode2
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object DetectionCode2
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised TrainingCode2
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical DomainsCode2
Binary Neural Networks: A SurveyCode2
LLM-FP4: 4-Bit Floating-Point Quantized TransformersCode2
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable ApproachesCode2
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language ModelsCode2
LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor SearchCode2
Lossless Compression of Vector IDs for Approximate Nearest Neighbor SearchCode2
Adapting Large Language Models by Integrating Collaborative Semantics for RecommendationCode2
Binarized Neural Machine TranslationCode2
INT-FlashAttention: Enabling Flash Attention for INT8 QuantizationCode2
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge UnderstandingCode2
I-ViT: Integer-only Quantization for Efficient Vision Transformer InferenceCode2
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block QuantizationCode2
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
Compressing Large Language Models using Low Rank and Low Precision DecompositionCode2
Imp: Highly Capable Large Multimodal Models for Mobile DevicesCode2
BitDecoding: Unlocking Tensor Cores for Long-Context LLMs Decoding with Low-Bit KV CacheCode2
Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive SurveyCode2
BHViT: Binarized Hybrid Vision TransformerCode2
I-BERT: Integer-only BERT QuantizationCode2
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group QuantizationCode2
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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