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 951–975 of 1356 papers

TitleStatusHype
OPTISHEAR: Towards Efficient and Adaptive Pruning of Large Language Models via Evolutionary Optimization—0
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models—0
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights—0
OTOV2: Automatic, Generic, User-Friendly—0
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling—0
Towards Higher Ranks via Adversarial Weight Pruning—0
Pacemaker: Intermediate Teacher Knowledge Distillation For On-The-Fly Convolutional Neural Network—0
Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs—0
Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression—0
AMD: Adaptive Masked Distillation for Object Detection—0
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning—0
Towards Modality Transferable Visual Information Representation with Optimal Model Compression—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting—0
Towards Optimal Compression: Joint Pruning and Quantization—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
Weight Squeezing: Reparameterization for Compression and Fast Inference—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—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