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

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Showing 1751–1800 of 4925 papers

TitleStatusHype
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data—0
Compressed Particle-Based Federated Bayesian Learning and Unlearning—0
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs—0
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition—0
Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks—0
Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content—0
Fighting Quantization Bias With Bias—0
Fighting over-fitting with quantization for learning deep neural networks on noisy labels—0
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference—0
A Robust Visual Sampling Model Inspired by Receptive Field—0
A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints—0
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs—0
3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D generation—0
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing—0
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification—0
Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment—0
FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification—0
ARM 4-BIT PQ: SIMD-based Acceleration for Approximate Nearest Neighbor Search on ARM—0
ADMM Based Semi-Structured Pattern Pruning Framework For Transformer—0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation—0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization—0
A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case—0
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization—0
FedHQ: Hybrid Runtime Quantization for Federated Learning—0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—0
A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits—0
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design—0
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks—0
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning—0
Federated Split BERT for Heterogeneous Text Classification—0
Composite Correlation Quantization for Efficient Multimodal Retrieval—0
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization—0
Composite Code Sparse Autoencoders for first stage retrieval—0
FewGAN: Generating from the Joint Distribution of a Few Images—0
Are Words the Quanta of Human Language? Extending the Domain of Quantum Cognition—0
Federated Learning With Quantized Global Model Updates—0
FFN Fusion: Rethinking Sequential Computation in Large Language Models—0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization—0
Component Training of Turbo Autoencoders—0
Federated Learning: Strategies for Improving Communication Efficiency—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video—0
Federated Learning in Adversarial Settings—0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission—0
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding—0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners—0
A Review of Recent Advances of Binary Neural Networks for Edge Computing—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