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 101–125 of 1356 papers

TitleStatusHype
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
Activation-Informed Merging of Large Language ModelsCode1
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
Attention Sinks and Outlier Features: A 'Catch, Tag, and Release' Mechanism for Embeddings—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
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
SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion TransformerCode9
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 ModelsCode0
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures—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
SWSC: Shared Weight for Similar Channel in LLM—0
A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor FusionCode1
Tensorization of neural networks for improved privacy and interpretabilityCode0
Merging Feed-Forward Sublayers for Compressed TransformersCode1
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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