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 3701–3725 of 4925 papers

TitleStatusHype
On Round-Off Errors and Gaussian Blur in Superresolution and in Image Registration—0
On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing—0
On the Adversarial Robustness of Quantized Neural Networks—0
On the Compressibility of Quantized Large Language Models—0
On the Compression of Language Models for Code: An Empirical Study on CodeBERT—0
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers—0
On the effectiveness of discrete representations in sparse mixture of experts—0
On the Effectiveness of Hybrid Mutual Information Estimation—0
On the Effect of Quantization on Dynamic Mode Decomposition—0
On the efficient representation and execution of deep acoustic models—0
On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision—0
On the Impact of Fixed Point Hardware for Optical Fiber Nonlinearity Compensation Algorithms—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''—0
On the Interplay of Privacy, Persuasion and Quantization—0
On the Logic Elements Associated with Round-Off Errors and Gaussian Blur in Image Registration: A Simple Case of Commingling—0
On the Needs for Rotations in Hypercubic Quantization Hashing—0
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective—0
On the Pareto Efficiency of Quantized CNN—0
On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels—0
On the Quantization of Cellular Neural Networks for Cyber-Physical Systems—0
On the quantization of recurrent neural networks—0
On the Relation Between Speech Quality and Quantized Latent Representations of Neural Codecs—0
On the relevance of language in speaker recognition—0
On the Role of Spatial Effects in Early Estimates of Disease Infectiousness: A Second Quantization Approach—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