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 1651–1675 of 4925 papers

TitleStatusHype
Continuous Control with Action Quantization from Demonstrations—0
Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis—0
Continuous Approximations for Improving Quantization Aware Training of LLMs—0
Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?—0
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies—0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking—0
A Survey on Deep Hashing Methods—0
A Formalization of Image Vectorization by Region Merging—0
Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning—0
A Survey of Techniques for Optimizing Transformer Inference—0
A Survey of Small Language Models—0
A Flexible, Extensible Software Framework for Neural Net Compression—0
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search—0
L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization—0
1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by Authentic All-optical Fully-connected Architecture—0
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry—0
Constraint Guided Model Quantization of Neural Networks—0
A Survey of Quantization Methods for Efficient Neural Network Inference—0
Constrained Approximate Similarity Search on Proximity Graph—0
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks—0
A Survey of Model Compression and Acceleration for Deep Neural Networks—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
A Survey of Methods for Low-Power Deep Learning and Computer Vision—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—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