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 1326–1350 of 4925 papers

TitleStatusHype
Harnessing Large Language Models Locally: Empirical Results and Implications for AI PCCode0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone NetworksCode0
Denoising Noisy Neural Networks: A Bayesian Approach with CompensationCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Guetzli: Perceptually Guided JPEG EncoderCode0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing UnitsCode0
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-OffCode0
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing SystemsCode0
HDRUNet: Single Image HDR Reconstruction with Denoising and DequantizationCode0
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMsCode0
Genie: Show Me the Data for QuantizationCode0
Deep Triplet QuantizationCode0
Deep Task-Based Analog-to-Digital ConversionCode0
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth GradientsCode0
Goten: GPU-Outsourcing Trusted Execution of Neural Network Training and PredictionCode0
Bag of Tricks for Optimizing Transformer EfficiencyCode0
DeepShift: Towards Multiplication-Less Neural NetworksCode0
General Point Model Pretraining with Autoencoding and AutoregressiveCode0
Deep reverse tone mappingCode0
Generalized Relevance Learning Grassmann QuantizationCode0
Deep residual network for steganalysis of digital imagesCode0
Deep Recurrent Quantization for Generating Sequential Binary CodesCode0
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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