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 1301–1325 of 1356 papers

TitleStatusHype
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Structured Model Pruning for Efficient Inference in Computational Pathology—0
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression—0
Bringing AI To Edge: From Deep Learning's Perspective—0
Structured Multi-Hashing for Model Compression—0
BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction—0
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces—0
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks—0
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments—0
Distilling with Performance Enhanced Students—0
Distributed Low Precision Training Without Mixed Precision—0
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization—0
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression—0
DLIP: Distilling Language-Image Pre-training—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—0
DNA data storage, sequencing data-carrying DNA—0
DNN Model Compression Under Accuracy Constraints—0
Does Learning Require Memorization? A Short Tale about a Long Tail—0
Domain Adaptation Regularization for Spectral Pruning—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—0
Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms—0
Dream Distillation: A Data-Independent Model Compression Framework—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