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 4751–4775 of 4925 papers

TitleStatusHype
Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological TransformerCode0
Uncovering the Hidden Cost of Model CompressionCode0
Power Law Graph Transformer for Machine Translation and Representation LearningCode0
The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language ModelsCode0
Vector and Line Quantization for Billion-scale Similarity Search on GPUsCode0
HQOD: Harmonious Quantization for Object DetectionCode0
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationCode0
PP-ShiTu: A Practical Lightweight Image Recognition SystemCode0
The Ecological Footprint of Neural Machine Translation SystemsCode0
Effective Quantization Methods for Recurrent Neural NetworksCode0
Effective Communication with Dynamic Feature CompressionCode0
Training Thinner and Deeper Neural Networks: Jumpstart RegularizationCode0
The Effect of Points Dispersion on the k-nn Search in Random Projection ForestsCode0
PQV-Mobile: A Combined Pruning and Quantization Toolkit to Optimize Vision Transformers for Mobile ApplicationsCode0
HyperFlow: Representing 3D Objects as SurfacesCode0
EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical ModelCode0
The Effect of Points Dispersion on the k-nn Search in Random Projection ForestsCode0
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
Sparsified SGD with MemoryCode0
Resource Constrained Semantic Segmentation for Waste SortingCode0
Cauchy-Schwarz RegularizersCode0
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and QuantizationCode0
CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent LayersCode0
The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal StatesCode0
Hybrid coarse-fine classification for head pose estimationCode0
Show:102550
← PrevPage 191 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