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 4226–4250 of 4925 papers

TitleStatusHype
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding—0
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference—0
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Discrete-Valued Neural Communication—0
Discrete-Valued Neural Networks Using Variational Inference—0
Discriminative Cross-View Binary Representation Learning—0
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Disentangling segmental and prosodic factors to non-native speech comprehensibility—0
DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex—0
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models—0
Distance-aware Quantization—0
Distance Encoded Product Quantization—0
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics—0
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics—0
Distortion-Controlled Dithering with Reduced Recompression Rate—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Splitting—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication—0
Show:102550
← PrevPage 170 of 197Next →
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