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 926–950 of 1356 papers

TitleStatusHype
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