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 2126–2150 of 4925 papers

TitleStatusHype
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Discriminative Cross-View Binary Representation Learning—0
Discrete-Valued Neural Networks Using Variational Inference—0
BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling—0
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training—0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques—0
Discrete-Valued Neural Communication—0
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks—0
Analysis of Quantized Models—0
Improving Quantization with Post-Training Model Expansion—0
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy—0
BF-IMNA: A Bit Fluid In-Memory Neural Architecture for Neural Network Acceleration—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
Analysis of Quantization on MLP-based Vision Models—0
Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach—0
Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs—0
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference—0
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding—0
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics—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