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

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Showing 901–950 of 4925 papers

TitleStatusHype
Achieving binary weight and activation for LLMs using Post-Training Quantization—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
A System-Level Solution for Low-Power Object Detection—0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation—0
A Channelized Binning Method for Extraction of Dominant Color Pixel Value—0
Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization—0
Asymptotic tracking control of dynamic reference over homomorphically encrypted data with finite modulus—0
AgileIR: Memory-Efficient Group Shifted Windows Attention for Agile Image Restoration—0
4-bit Quantization of LSTM-based Speech Recognition Models—0
Asymptotic stabilization under homomorphic encryption: A re-encryption free method—0
Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network—0
Aggressive Post-Training Compression on Extremely Large Language Models—0
Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems—0
Asymptotically Optimal Closed-Form Phase Configuration of 1-bit RISs via Sign Alignment—0
Aggregating empirical evidence from data strategy studies: a case on model quantization—0
Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data—0
Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces—0
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering—0
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization—0
Asymmetric Deep Semantic Quantization for Image Retrieval—0
Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval—0
L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization—0
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations—0
A Generalized Zero-Shot Quantization of Deep Convolutional Neural Networks via Learned Weights Statistics—0
A Survey on Transformer Compression—0
A Survey on Model Compression for Large Language Models—0
A General Family of Stochastic Proximal Gradient Methods for Deep Learning—0
Accurate INT8 Training Through Dynamic Block-Level Fallback—0
A Survey on Methods and Theories of Quantized Neural Networks—0
A Survey on Learning to Hash—0
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies—0
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search—0
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking—0
A Survey on Deep Hashing Methods—0
A Formalization of Image Vectorization by Region Merging—0
A Survey of Techniques for Optimizing Transformer Inference—0
A Survey of Small Language Models—0
A Flexible, Extensible Software Framework for Neural Net Compression—0
1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by Authentic All-optical Fully-connected Architecture—0
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
A Survey of Quantization Methods for Efficient Neural Network Inference—0
A Survey of Model Compression and Acceleration for Deep Neural Networks—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
A Survey of Methods for Low-Power Deep Learning and Computer Vision—0
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification—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