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 951–1000 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
Towards Superior Quantization Accuracy: A Layer-sensitive Approach—0
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices—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
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models—0
A Low Effort Approach to Structured CNN Design Using PCA—0
Do we need Label Regularization to Fine-tune Pre-trained Language Models?—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding—0
Position-Aware Depth Decay Decoding (D^3): Boosting Large Language Model Inference Efficiency—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—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
What do larger image classifiers memorise?—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