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 1901–1950 of 4925 papers

TitleStatusHype
FullPack: Full Vector Utilization for Sub-Byte Quantized Inference on General Purpose CPUs—0
Full-Precision Free Binary Graph Neural Networks—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
Fully Digital Second-order Level-crossing Sampling ADC for Data Saving in Sensing Sparse Signals—0
FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression—0
Homomorphic Encryption-Enabled Distance-Based Distributed Formation Control with Distance Mismatch Estimators—0
FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?—0
FD Cell-Free mMIMO: Analysis and Optimization—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Functional Invariants to Watermark Large Transformers—0
Functional quantization of rough volatility and applications to volatility derivatives—0
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach—0
Fundamental Trade-offs in Quantized Hybrid Radar Fusion: A CRB-Rate Perspective—0
FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations—0
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering—0
Fuzzy Norm-Explicit Product Quantization for Recommender Systems—0
FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation—0
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices—0
FBQuant: FeedBack Quantization for Large Language Models—0
Compact Representation for Image Classification: To Choose or to Compress?—0
GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization—0
FBI: Fingerprinting models with Benign Inputs—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
GAQAT: gradient-adaptive quantization-aware training for domain generalization—0
Gaussian Approximation of Quantization Error for Estimation from Compressed Data—0
Are disentangled representations all you need to build speaker anonymization systems?—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Accelerator-Aware Training for Transducer-Based Speech Recognition—0
Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization—0
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems—0
GDRQ: Group-based Distribution Reshaping for Quantization—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
FATNN: Fast and Accurate Ternary Neural Networks—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation—0
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization—0
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks—0
Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs—0
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation—0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
Fast Template Evaluation with Vector Quantization—0
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding—0
A Deep Hashing Learning Network—0
FastSGD: A Fast Compressed SGD Framework for Distributed Machine Learning—0
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference—0
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC—0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior—0
Fast Orthogonal Projection Based on Kronecker Product—0
Show:102550
← PrevPage 39 of 99Next →
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