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 701–725 of 4925 papers

TitleStatusHype
BiDM: Pushing the Limit of Quantization for Diffusion ModelsCode1
SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization—0
Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques—0
Sensor Selection and Distributed Quantization for Energy Efficiency in Massive MTC—0
Error Feedback Approach for Quantization Noise Reduction of Distributed Graph Filters—0
ULMRec: User-centric Large Language Model for Sequential Recommendation—0
GAQAT: gradient-adaptive quantization-aware training for domain generalization—0
Temporally Compressed 3D Gaussian Splatting for Dynamic ScenesCode1
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search—0
APOLLO: SGD-like Memory, AdamW-level PerformanceCode3
QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint VideosCode2
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization—0
Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task—0
Prompting Large Language Models for Clinical Temporal Relation Extraction—0
Designing DNNs for a trade-off between robustness and processing performance in embedded devices—0
Mixed-Precision Quantization: Make the Best Use of Bits Where They Matter Most—0
Unifying KV Cache Compression for Large Language Models with LeanKV—0
TokenFlow: Unified Image Tokenizer for Multimodal Understanding and GenerationCode3
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness—0
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectiveCode0
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models—0
Robust Precoding for Multi-User Visible Light Communications with Quantized Channel Information—0
3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D generation—0
Lean classical-quantum hybrid neural network model for image classification—0
Taming Scalable Visual Tokenizer for Autoregressive Image GenerationCode4
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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