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 1351–1375 of 4925 papers

TitleStatusHype
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks—0
Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?—0
A Novel Physics-based Channel Model for Reconfigurable Intelligent Surface-assisted Multi-user Communication Systems—0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks—0
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data—0
CAMBI: Contrast-aware Multiscale Banding Index—0
A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays—0
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design—0
A Novel Hybrid Precoder With Low-Resolution Phase Shifters and Fronthaul Capacity Limitation—0
Adaptive Integrate-and-Fire Time Encoding Machine with Quantization—0
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime—0
A Novel Framework for Image-to-image Translation and Image Compression—0
CacheQuant: Comprehensively Accelerated Diffusion Models—0
A Novel Chaotic Uniform Quantizer for Speech Coding—0
Accelerated Distance Computation with Encoding Tree for High Dimensional Data—0
ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting—0
Emotion Recognition Using Speaker Cues—0
A Novel Audio Representation for Music Genre Identification in MIR—0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge—0
Byzantine-Resilient Secure Federated Learning—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Adaptive Dither Voting for Robust Spatial Verification—0
Bullion: A Column Store for Machine Learning—0
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers—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