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
BinaryBERT: Pushing the Limit of BERT Quantization—0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Developing Far-Field Speaker System Via Teacher-Student Learning—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey—0
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
An Automatic and Efficient BERT Pruning for Edge AI Systems—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
Analysis of Quantization on MLP-based Vision Models—0
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles—0
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments—0
Beware of Calibration Data for Pruning Large Language Models—0
Analysis of memory consumption by neural networks based on hyperparameters—0
Benchmarking Adversarial Robustness of Compressed Deep Learning Models—0
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers—0
ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation—0
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models—0
BD-KD: Balancing the Divergences for Online Knowledge Distillation—0
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design—0
Activation Sparsity Opportunities for Compressing General Large Language Models—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing—0
A Model Compression Method with Matrix Product Operators for Speech Enhancement—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