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 4901–4925 of 4925 papers

TitleStatusHype
Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space—0
Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart—0
Improving Quantization with Post-Training Model Expansion—0
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks—0
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images—0
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training—0
IMSSA: Deploying modern state-space models on memristive in-memory compute hardware—0
Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes—0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques—0
Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation—0
Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data—0
Incremental few-shot learning via vector quantization in deep embedded space—0
Incremental Learning Through Deep Adaptation—0
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs—0
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning—0
End-to-End Supervised Product Quantization for Image Search and Retrieval—0
In-Depth DCT Coefficient Distribution Analysis for First Quantization Estimation—0
Indexing of CNN Features for Large Scale Image Search—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Informational Embodiment: Computational role of information structure in codes and robots—0
Information and Statistical Efficiency When Quantizing Noisy DC Values—0
Information Extraction Under Privacy Constraints—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