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 751–800 of 1356 papers

TitleStatusHype
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy—0
Model Blending for Text Classification—0
Quiver neural networks—0
Efficient model compression with Random Operation Access Specific Tile (ROAST) hashingCode0
Model Compression for Resource-Constrained Mobile Robots—0
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
Normalized Feature Distillation for Semantic Segmentation—0
Rank-Based Filter Pruning for Real-Time UAV Tracking—0
Quantum Neural Network Compression—0
KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation—0
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early ExitingCode0
Language model compression with weighted low-rank factorization—0
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach—0
Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification—0
An Automatic and Efficient BERT Pruning for Edge AI Systems—0
Knowledge Distillation for Oriented Object Detection on Aerial Images—0
Revisiting Self-Distillation—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks—0
STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor DecompositionCode0
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation—0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask—0
Differentially Private Model Compression—0
Canonical convolutional neural networksCode0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate—0
MiniDisc: Minimal Distillation Schedule for Language Model CompressionCode0
Do we need Label Regularization to Fine-tune Pre-trained Language Models?—0
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models—0
Aligning Logits Generatively for Principled Black-Box Knowledge DistillationCode0
InDistill: Information flow-preserving knowledge distillation for model compressionCode0
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey—0
Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data—0
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators—0
Chemical transformer compression for accelerating both training and inference of molecular modelingCode0
DNA data storage, sequencing data-carrying DNA—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Online Model Compression for Federated Learning with Large Models—0
Can collaborative learning be private, robust and scalable?—0
Multi-Granularity Structural Knowledge Distillation for Language Model CompressionCode0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Neural Network Pruning by Cooperative Coevolution—0
Characterizing and Understanding the Behavior of Quantized Models for Reliable DeploymentCode0
Enabling All In-Edge Deep Learning: A Literature Review—0
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured SparsificationCode0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—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