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 901–950 of 1356 papers

TitleStatusHype
YANMTT: Yet Another Neural Machine Translation Toolkit—0
Small Object Detection Based on Modified FSSD and Model Compression—0
Scaling Laws for Deep Learning—0
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicesCode0
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data—0
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained HardwareCode0
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000 Compression and 3.1 Faster Inference—0
Learning a Neural Diff for Speech Models—0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning—0
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework—0
Pruning Ternary Quantization—0
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—0
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks—0
Federated Action Recognition on Heterogeneous Embedded Devices—0
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices—0
Compact and Optimal Deep Learning with Recurrent Parameter GeneratorsCode0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations—0
Universal approximation and model compression for radial neural networksCode0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural NetworksCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams—0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
Network Pruning via Performance MaximizationCode0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Quantized Neural Networks via -1, +1 Encoding Decomposition and AccelerationCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Topology Distillation for Recommender System—0
Masked Training of Neural Networks with Partial Gradients—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
FedNL: Making Newton-Type Methods Applicable to Federated Learning—0
Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks—0
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
On Attention Redundancy: A Comprehensive Study—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization—0
Differentiable Sparsification for Deep Neural Networks—0
Model Compression—0
How to Explain Neural Networks: an Approximation Perspective—0
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration—0
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation—0
Neural 3D Scene Compression via Model Compression—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