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 4726–4750 of 4925 papers

TitleStatusHype
Billion-scale similarity search with GPUsCode4
Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment—0
Fixed-point optimization of deep neural networks with adaptive step size retraining—0
Low-Precision Batch-Normalized Activations—0
Efficient Large-scale Approximate Nearest Neighbor Search on the GPUCode0
Soft Weight-Sharing for Neural Network CompressionCode0
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision WeightsCode0
Hashing in the Zero Shot Framework with Domain Adaptation—0
Deep Learning with Low Precision by Half-wave Gaussian QuantizationCode0
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point—0
Scalable Nearest Neighbor Search based on kNN Graph—0
Compression of Deep Neural Networks for Image Instance Retrieval—0
Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images—0
Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses—0
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics—0
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction—0
Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection—0
Deep Residual Hashing—0
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks—0
FastText.zip: Compressing text classification modelsCode1
Towards the Limit of Network Quantization—0
Trained Ternary QuantizationCode1
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity—0
Clustering with Bregman Divergences: an Asymptotic Analysis—0
Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization—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