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 676–700 of 1356 papers

TitleStatusHype
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
OTOV2: Automatic, Generic, User-Friendly—0
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence GuaranteeCode0
Greener yet Powerful: Taming Large Code Generation Models with Quantization—0
Gradient-Free Structured Pruning with Unlabeled Data—0
Rotation Invariant Quantization for Model CompressionCode0
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms—0
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation—0
Debiased Distillation by Transplanting the Last Layer—0
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach—0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—0
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques—0
Towards Optimal Compression: Joint Pruning and Quantization—0
On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence—0
Knowledge Distillation in Vision Transformers: A Critical Review—0
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications—0
Knowledge Distillation on Graphs: A Survey—0
AMD: Adaptive Masked Distillation for Object Detection—0
Improved knowledge distillation by utilizing backward pass knowledge in neural networks—0
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks—0
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble—0
Distilling Focal Knowledge From Imperfect Expert for 3D Object DetectionCode0
One-Shot Model for Mixed-Precision Quantization—0
Tiny Updater: Towards Efficient Neural Network-Driven Software UpdatingCode0
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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