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 2451–2475 of 4925 papers

TitleStatusHype
Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning—0
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication—0
HDR Imaging With One-Bit Quantization—0
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization—0
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models—0
Bandwidth-efficient Inference for Neural Image Compression—0
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems—0
QuantEase: Optimization-based Quantization for Language Models—0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking—0
On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks—0
On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision—0
Softmax Bias Correction for Quantized Generative Models—0
eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models—0
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector QuantizationCode0
Implementation and Evaluation of Physical Layer Key Generation on SDR based LoRa Platform—0
FPTQ: Fine-grained Post-Training Quantization for Large Language Models—0
Uncovering the Hidden Cost of Model CompressionCode0
Continual Learning for Generative Retrieval over Dynamic CorporaCode0
Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message PropagationCode0
On-Device Learning with Binary Neural Networks—0
MEMORY-VQ: Compression for Tractable Internet-Scale Memory—0
Maestro: Uncovering Low-Rank Structures via Trainable DecompositionCode0
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow AvoidanceCode0
Efficient Learned Lossless JPEG Recompression—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