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 551–575 of 1356 papers

TitleStatusHype
Adaptive Neural Connections for Sparsity Learning—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking—0
Dynamic Model Pruning with Feedback—0
Can Students Beyond The Teacher? Distilling Knowledge from Teacher's Bias—0
A "Network Pruning Network" Approach to Deep Model Compression—0
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices—0
Can Model Compression Improve NLP Fairness—0
An Empirical Study of Low Precision Quantization for TinyML—0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient—0
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—0
Dual sparse training framework: inducing activation map sparsity via Transformed 1 regularization—0
Can collaborative learning be private, robust and scalable?—0
Dual Discriminator Adversarial Distillation for Data-free Model Compression—0
CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs—0
Stochastic Model Pruning via Weight Dropping Away and Back—0
Dreaming To Prune Image Deraining Networks—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Dream Distillation: A Data-Independent Model Compression Framework—0
Bringing AI To Edge: From Deep Learning's Perspective—0
An Empirical Investigation of Matrix Factorization Methods for Pre-trained Transformers—0
Adapting Models to Signal Degradation using Distillation—0
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction—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