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 2776–2800 of 4925 papers

TitleStatusHype
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
Security-Aware Approximate Spiking Neural Networks—0
UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion—0
Transceiver Cooperative Learning-aided Semantic Communications Against Mismatched Background Knowledge Bases—0
Does compressing activations help model parallel training?—0
Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering—0
Automating Nearest Neighbor Search Configuration with Constrained Optimization—0
Reduced Reference Quality Assessment for Point Cloud Compression—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Overcoming Forgetting Catastrophe in Quantization-Aware Training—0
Rethinking Few-Shot Medical Segmentation: A Vector Quantization View—0
One-Shot Model for Mixed-Precision Quantization—0
Adverse Weather Removal with Codebook Priors—0
Video Compression With Entropy-Constrained Neural Representations—0
Unsupervised Facial Performance Editing via Vector-Quantized StyleGAN Representations—0
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers—0
Deep Hashing With Minimal-Distance-Separated Hash Centers—0
Bit-Shrinking: Limiting Instantaneous Sharpness for Improving Post-Training Quantization—0
Toward Accurate Post-Training Quantization for Image Super ResolutionCode0
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Vector Quantization With Self-Attention for Quality-Independent Representation Learning—0
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise ModelingCode0
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery—0
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling—0
Biologically Plausible Learning on Neuromorphic Hardware Architectures—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