SOTAVerified

Model Compression

Model Compression is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.

Source: KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1125 of 1356 papers

TitleStatusHype
Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs—0
Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory—0
Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression—0
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning—0
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting—0
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation—0
PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators—0
PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices—0
Pea-KD: Parameter-efficient and Accurate Knowledge Distillation on BERT—0
Pea-KD: Parameter-efficient and accurate Knowledge Distillation—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—0
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs—0
PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices—0
Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data—0
PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration—0
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models—0
Position-Aware Depth Decay Decoding (D^3): Boosting Large Language Model Inference Efficiency—0
Post-Training Quantization for Video Matting—0
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs—0
Post-Training Weighted Quantization of Neural Networks for Language Models—0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
Practical quantum federated learning and its experimental demonstration—0
Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection—0
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data—0
Preview-based Category Contrastive Learning for Knowledge Distillation—0
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All datasetsImageNetQNLI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ADLIK-MO-ResNet50+W4A4Top-177.88—Unverified
2ADLIK-MO-ResNet50+W3A4Top-177.34—Unverified
3ResNet-18 + 4bit-1dim model compression using DKMTop-170.52—Unverified
4MobileNet-v1 + 4bit-1dim model compression using DKMTop-169.63—Unverified
5ResNet-18 + 2bit-1dim model compression using DKMTop-168.63—Unverified
6MobileNet-v1 + 2bit-1dim model compression using DKMTop-167.62—Unverified
7ResNet-18 + 4bit-4dim model compression using DKMTop-166.1—Unverified
8ResNet-18 + 2bit-2dim model compression using DKMTop-164.7—Unverified
9MobileNet-v1 + 4bit-4dim model compression using DKMTop-161.4—Unverified
10ResNet-18 + 1bit-1dim model compression using DKMTop-159.7—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileBERT + 2bit-1dim model compression using DKMAccuracy82.13—Unverified
2MobileBERT + 1bit-1dim model compression using DKMAccuracy63.17—Unverified