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 501–525 of 1356 papers

TitleStatusHype
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation—0
Efficient Model Compression for Hierarchical Federated Learning—0
Efficient Model Compression for Bayesian Neural Networks—0
Efficient Memory Management for GPU-based Deep Learning Systems—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity—0
Exploration and Estimation for Model Compression—0
A Partial Regularization Method for Network Compression—0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
Efficient DNN-Powered Software with Fair Sparse Models—0
Closed-Loop Neural Interfaces with Embedded Machine Learning—0
Efficient Computation of Quantized Neural Networks by −1, +1 Encoding Decomposition—0
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications—0
An Overview of Neural Network Compression—0
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
2-bit Conformer quantization for automatic speech recognition—0
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection—0
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices—0
Efficient Apple Maturity and Damage Assessment: A Lightweight Detection Model with GAN and Attention Mechanism—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—0
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks—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