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 826–850 of 1356 papers

TitleStatusHype
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
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks—0
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—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
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Universal approximation and model compression for radial neural networksCode0
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
Learning Efficient Vision Transformers via Fine-Grained Manifold DistillationCode1
Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural NetworksCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams—0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
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
Learning Student Networks in the WildCode2
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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