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 2201–2225 of 4925 papers

TitleStatusHype
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics—0
Reducing Communication for Split Learning by Randomized Top-k Sparsification—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
BRICS: Bi-level feature Representation of Image CollectionS—0
LLM-QAT: Data-Free Quantization Aware Training for Large Language ModelsCode3
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
Reversible Quantization Index Modulation for Static Deep Neural Network Watermarking—0
Disentanglement via Latent QuantizationCode1
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing—0
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals—0
2-bit Conformer quantization for automatic speech recognition—0
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time—0
PQA: Exploring the Potential of Product Quantization in DNN Hardware AccelerationCode0
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range MultilaterationCode1
NVTC: Nonlinear Vector Transform CodingCode1
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Just CHOP: Embarrassingly Simple LLM Compression—0
BinaryViT: Towards Efficient and Accurate Binary Vision Transformers—0
QLoRA: Efficient Finetuning of Quantized LLMsCode6
Adversarial Defenses via Vector Quantization—0
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image GenerationCode1
Downlink Clustering-Based Scheduling of IRS-Assisted Communications With Reconfiguration Constraints—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Differential Privacy with Random Projections and Sign Random Projections—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