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 1926–1950 of 4925 papers

TitleStatusHype
QuATON: Quantization Aware Training of Optical Neurons—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization—0
Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness—0
Discrete, compositional, and symbolic representations through attractor dynamicsCode0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
Compressing LLMs: The Truth is Rarely Pure and Never SimpleCode1
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device—0
DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex—0
Quantization of Deep Neural Networks to facilitate self-correction of weights on Phase Change Memory-based analog hardware—0
One-Bit Channel Estimation for IRS-aided Millimeter-Wave Massive MU-MISO System—0
Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMsCode1
Revolutionizing Mobile Interaction: Enabling a 3 Billion Parameter GPT LLM on Mobile—0
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic DecompositionCode1
Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution HeadCode1
On Uniform Scalar Quantization for Learned Image Compression—0
Diffusion Models as Stochastic Quantization in Lattice Field TheoryCode0
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural RepresentationsCode1
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings—0
PB-LLM: Partially Binarized Large Language ModelsCode1
MixQuant: Mixed Precision Quantization with a Bit-width Optimization Search—0
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities—0
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular QuantizersCode2
Transformer-VQ: Linear-Time Transformers via Vector QuantizationCode2
Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language ModelsCode0
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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