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 751–775 of 4925 papers

TitleStatusHype
Differentiable Model Compression via Pseudo Quantization NoiseCode1
Conditional Coding and Variable Bitrate for Practical Learned Video CodingCode1
Matching-oriented Product Quantization For Ad-hoc RetrievalCode1
Distributed Learning Systems with First-order MethodsCode1
Quantized Gromov-WassersteinCode1
Network Quantization with Element-wise Gradient ScalingCode1
Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric QuantizerCode1
Integer-only Zero-shot Quantization for Efficient Speech RecognitionCode1
ReCU: Reviving the Dead Weights in Binary Neural NetworksCode1
RangeDet:In Defense of Range View for LiDAR-based 3D Object DetectionCode1
Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAECode1
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted NetworkCode1
Learning Statistical Texture for Semantic SegmentationCode1
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained DevicesCode1
Self-Distribution Binary Neural NetworksCode1
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network QuantizationCode1
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware TransformationCode1
Confounding Tradeoffs for Neural Network QuantizationCode1
BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionCode1
On the Universal Transformation of Data-Driven Models to Control SystemsCode1
Enabling Binary Neural Network Training on the EdgeCode1
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded PlatformsCode1
Benchmarking Quantized Neural Networks on FPGAs with FINNCode1
SparseDNN: Fast Sparse Deep Learning Inference on CPUsCode1
Binary TTC: A Temporal Geofence for Autonomous NavigationCode1
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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