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 3826–3850 of 4925 papers

TitleStatusHype
PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions—0
PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization—0
Pixel Embedding: Fully Quantized Convolutional Neural Network with Differentiable Lookup Table—0
Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data—0
PKLot-A robust dataset for parking lot classification—0
Plug-and-Play 1.x-Bit KV Cache Quantization for Video Large Language Models—0
PoET-BiN: Power Efficient Tiny Binary Neurons—0
PoGO: A Scalable Proof of Useful Work via Quantized Gradient Descent and Merkle Proofs—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos—0
Poster: Self-Supervised Quantization-Aware Knowledge Distillation—0
Posthoc Interpretation via Quantization—0
Post-Training 4-bit Quantization on Embedding Tables—0
Post-Training Non-Uniform Quantization for Convolutional Neural Networks—0
Post-Training Quantization for Cross-Platform Learned Image Compression—0
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping—0
Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks—0
Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric—0
Post-Training Quantization for Video Matting—0
Post-Training Quantization for Vision Transformer—0
Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization—0
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression—0
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs—0
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision—0
Post-Training Weighted Quantization of Neural Networks for Language Models—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