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 3501–3525 of 4925 papers

TitleStatusHype
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers—0
A Deep Hashing Learning Network—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation—0
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video—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
ADMM Based Semi-Structured Pattern Pruning Framework For Transformer—0
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
Adversarial Defenses via Vector Quantization—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
Adverse Weather Removal with Codebook Priors—0
A Faster Approach to Spiking Deep Convolutional Neural Networks—0
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting—0
A Feature-map Discriminant Perspective for Pruning Deep Neural Networks—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
A Flexible, Extensible Software Framework for Neural Net Compression—0
A Formalization of Image Vectorization by Region Merging—0
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies—0
A General Family of Stochastic Proximal Gradient Methods for Deep Learning—0
A Generalized Zero-Shot Quantization of Deep Convolutional Neural Networks via Learned Weights Statistics—0
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization—0
Aggregating empirical evidence from data strategy studies: a case on model quantization—0
Show:102550
← PrevPage 141 of 197Next →
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