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 901–925 of 4925 papers

TitleStatusHype
Achieving binary weight and activation for LLMs using Post-Training Quantization—0
Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain—0
A System-Level Solution for Low-Power Object Detection—0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation—0
A Channelized Binning Method for Extraction of Dominant Color Pixel Value—0
Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization—0
Asymptotic tracking control of dynamic reference over homomorphically encrypted data with finite modulus—0
AgileIR: Memory-Efficient Group Shifted Windows Attention for Agile Image Restoration—0
4-bit Quantization of LSTM-based Speech Recognition Models—0
Asymptotic stabilization under homomorphic encryption: A re-encryption free method—0
Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network—0
Aggressive Post-Training Compression on Extremely Large Language Models—0
Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems—0
Asymptotically Optimal Closed-Form Phase Configuration of 1-bit RISs via Sign Alignment—0
Aggregating empirical evidence from data strategy studies: a case on model quantization—0
Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data—0
Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations—0
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks—0
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces—0
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering—0
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization—0
Asymmetric Deep Semantic Quantization for Image Retrieval—0
Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval—0
L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization—0
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations—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