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 1051–1075 of 1356 papers

TitleStatusHype
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Neural Network Compression for Noisy Storage Devices—0
Neural Network Compression using Binarization and Few Full-Precision Weights—0
Neural Network Compression Via Sparse Optimization—0
Neural Network Pruning by Cooperative Coevolution—0
Neural Regularized Domain Adaptation for Chinese Word Segmentation—0
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing—0
Noisy Neural Network Compression for Analog Storage Devices—0
Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads—0
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?—0
Normalized Feature Distillation for Semantic Segmentation—0
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models—0
NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry—0
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models—0
NVRC: Neural Video Representation Compression—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence—0
Data-Independent Neural Pruning via Coresets—0
On Attention Redundancy: A Comprehensive Study—0
Onboard Optimization and Learning: A Survey—0
Once-Tuning-Multiple-Variants: Tuning Once and Expanded as Multiple Vision-Language Model Variants—0
On-Device Document Classification using multimodal features—0
On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration—0
One-Shot Model for Mixed-Precision Quantization—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