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 2276–2300 of 4925 papers

TitleStatusHype
Defensive Quantization: When Efficiency Meets Robustness—0
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model—0
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions—0
Deep Visual-Semantic Quantization for Efficient Image Retrieval—0
Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection—0
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback—0
A Comprehensive Survey of Compression Algorithms for Language Models—0
KurTail : Kurtosis-based LLM Quantization—0
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming—0
Deep Unfolding with Kernel-based Quantization in MIMO Detection—0
Balance of Number of Embedding and their Dimensions in Vector Quantization—0
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks—0
Alternating Multi-bit Quantization for Recurrent Neural Networks—0
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning—0
Deep Task-Based Quantization—0
Deep Spherical Quantization for Image Search—0
Bag of Tricks with Quantized Convolutional Neural Networks for image classification—0
Alternating Direction Method of Multipliers for Quantization—0
Koopman Meets Limited Bandwidth: Effect of Quantization on Data-Driven Linear Prediction and Control of Nonlinear Systems—0
Deep Signal Recovery with One-Bit Quantization—0
Alternating Co-Quantization for Cross-Modal Hashing—0
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes—0
Deep Saliency Hashing—0
Back to Simplicity: How to Train Accurate BNNs from Scratch?—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