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 4426–4450 of 4925 papers

TitleStatusHype
Normalization Helps Training of Quantized LSTMCode0
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product SearchCode0
Accelerating Error Correction Code TransformersCode0
Loss-aware Weight Quantization of Deep NetworksCode0
Winner-takes-all learners are geometry-aware conditional density estimatorsCode0
Conditional COT-GAN for Video Prediction with Kernel SmoothingCode0
Loss Aware Post-training QuantizationCode0
A Bag-of-Words Equivalent Recurrent Neural Network for Action RecognitionCode0
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural NetworksCode0
Log-Time K-Means Clustering for 1D Data: Novel Approaches with Proof and ImplementationCode0
Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniquesCode0
What Do Compressed Deep Neural Networks Forget?Code0
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and QuantizationCode0
NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform QuantizationCode0
Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality GapCode0
Towards Effective Low-bitwidth Convolutional Neural NetworksCode0
LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-induced Risks and VulnerabilitiesCode0
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise ReductionCode0
Towards Efficient Active Learning of PDFACode0
Exploiting the Partly Scratch-off Lottery Ticket for Quantization-Aware TrainingCode0
Adaptive Prediction-Powered AutoEval with Reliability and Efficiency GuaranteesCode0
Depthwise Discrete Representation LearningCode0
LISA: Learning Interpretable Skill Abstractions from LanguageCode0
A2Q+: Improving Accumulator-Aware Weight QuantizationCode0
Denoising Noisy Neural Networks: A Bayesian Approach with CompensationCode0
Show:102550
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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