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 926–950 of 4925 papers

TitleStatusHype
A Generalized Zero-Shot Quantization of Deep Convolutional Neural Networks via Learned Weights Statistics—0
A Survey on Transformer Compression—0
A Survey on Model Compression for Large Language Models—0
A General Family of Stochastic Proximal Gradient Methods for Deep Learning—0
Accurate INT8 Training Through Dynamic Block-Level Fallback—0
A Survey on Methods and Theories of Quantized Neural Networks—0
A Survey on Learning to Hash—0
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies—0
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search—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
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
1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by Authentic All-optical Fully-connected Architecture—0
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
A Survey of Quantization Methods for Efficient Neural Network Inference—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
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification—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