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 301–325 of 1356 papers

TitleStatusHype
Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs—0
Systematic Outliers in Large Language ModelsCode0
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference—0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Attention Sinks and Outlier Features: A 'Catch, Tag, and Release' Mechanism for Embeddings—0
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression—0
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models—0
Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural NetworksCode0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning—0
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models—0
Practical quantum federated learning and its experimental demonstration—0
MultiPruner: Balanced Structure Removal in Foundation Models—0
FASP: Fast and Accurate Structured Pruning of Large Language Models—0
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images—0
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures—0
SWSC: Shared Weight for Similar Channel in LLM—0
Tensorization of neural networks for improved privacy and interpretabilityCode0
Neural Architecture Codesign for Fast Physics ApplicationsCode0
CURing Large Models: Compression via CUR Decomposition—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