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 526–550 of 1356 papers

TitleStatusHype
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection—0
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models—0
Adaptive Learning of Tensor Network Structures—0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates—0
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models—0
Education distillation:getting student models to learn in shcools—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series—0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
Edge Deep Learning for Neural Implants—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
An Improving Framework of regularization for Network Compression—0
Adaptive Quantization of Neural Networks—0
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization—0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
EDCompress: Energy-Aware Model Compression for Dataflows—0
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models—0
Cascaded channel pruning using hierarchical self-distillation—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation—0
Can We Find Strong Lottery Tickets in Generative Models?—0
A New Clustering-Based Technique for the Acceleration of Deep Convolutional 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