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 76–100 of 4925 papers

TitleStatusHype
APOLLO: SGD-like Memory, AdamW-level PerformanceCode3
TokenFlow: Unified Image Tokenizer for Multimodal Understanding and GenerationCode3
XQ-GAN: An Open-source Image Tokenization Framework for Autoregressive GenerationCode3
Scaling Transformers for Low-Bitrate High-Quality Speech CodingCode3
Pushing the Limits of Large Language Model Quantization via the Linearity TheoremCode3
Addressing Representation Collapse in Vector Quantized Models with One Linear LayerCode3
Data Generation for Hardware-Friendly Post-Training QuantizationCode3
COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 TrainingCode3
DPLM-2: A Multimodal Diffusion Protein Language ModelCode3
Latent Action Pretraining from VideosCode3
FlatQuant: Flatness Matters for LLM QuantizationCode3
ImageFolder: Autoregressive Image Generation with Folded TokensCode3
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style ControlCode3
BigCodec: Pushing the Limits of Low-Bitrate Neural Speech CodecCode3
TinyAgent: Function Calling at the EdgeCode3
Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language ModelCode3
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language ModelsCode3
Compact 3D Gaussian Splatting for Static and Dynamic Radiance FieldsCode3
Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object DetectionCode3
Fast Matrix Multiplications for Lookup Table-Quantized LLMsCode3
EfficientQAT: Efficient Quantization-Aware Training for Large Language ModelsCode3
Image and Video Tokenization with Binary Spherical QuantizationCode3
CV-VAE: A Compatible Video VAE for Latent Generative Video ModelsCode3
Ditto: Quantization-aware Secure Inference of Transformers upon MPCCode3
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of ExpertsCode3
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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