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 2726–2750 of 4925 papers

TitleStatusHype
Scaling Laws for Floating Point Quantization Training—0
Scaling Laws for Mixed quantization in Large Language Models—0
Scaling Laws for Post Training Quantized Large Language Models—0
Scaling Up Deep Neural Network Optimization for Edge Inference—0
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge—0
SceneSqueezer: Learning To Compress Scene for Camera Relocalization—0
ScionFL: Efficient and Robust Secure Quantized Aggregation—0
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time—0
sDAC -- Semantic Digital Analog Converter for Semantic Communications—0
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training—0
SDQ: Sparse Decomposed Quantization for LLM Inference—0
SDQ: Stochastic Differentiable Quantization with Mixed Precision—0
SDR: Efficient Neural Re-ranking using Succinct Document Representation—0
SEAL: SEmantic-Augmented Imitation Learning via Language Model—0
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization—0
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization—0
Secret Lies in Color: Enhancing AI-Generated Images Detection with Color Distribution Analysis—0
Secure Evaluation of Quantized Neural Networks—0
Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization—0
Security-Aware Approximate Spiking Neural Networks—0
Seeing Delta Parameters as JPEG Images: Data-Free Delta Compression with Discrete Cosine Transform—0
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods—0
SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization—0
Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images—0
Segmentation-Variant Codebooks for Preservation of Paralinguistic and Prosodic Information—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