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 1001–1025 of 4925 papers

TitleStatusHype
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor SearchCode2
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling—0
Forearm Ultrasound based Gesture Recognition on Edge—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation—0
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy—0
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare—0
Robust Training of Neural Networks at Arbitrary Precision and Sparsity—0
S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-trainingCode2
Investigating Disentanglement in a Phoneme-level Speech Codec for Prosody Modeling—0
Dequantization of a signal from two parallel quantized observations—0
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning—0
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters—0
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression—0
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM—0
NVRC: Neural Video Representation Compression—0
AgileIR: Memory-Efficient Group Shifted Windows Attention for Agile Image Restoration—0
Rate-Constrained Quantization for Communication-Efficient Federated Learning—0
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement—0
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
BigCodec: Pushing the Limits of Low-Bitrate Neural Speech CodecCode3
Estimating the Completeness of Discrete Speech Units—0
SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook—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