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 1126–1150 of 1356 papers

TitleStatusHype
Semantic Retention and Extreme Compression in LLMs: Can We Have Both?—0
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers—0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points—0
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition—0
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
SGAD: Soft-Guided Adaptively-Dropped Neural Network—0
Unleashing Channel Potential: Space-Frequency Selection Convolution for SAR Object Detection—0
Unraveling Key Factors of Knowledge Distillation—0
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems—0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient—0
Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction—0
Adapting Models to Signal Degradation using Distillation—0
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
Unsupervised model compression for multilayer bootstrap networks—0
Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing—0
Masked Training of Neural Networks with Partial Gradients—0
UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles—0
AdapMTL: Adaptive Pruning Framework for Multitask Learning Model—0
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation—0
USDC: Unified Static and Dynamic Compression for Visual Transformer—0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—0
Small, Accurate, and Fast Vehicle Re-ID on the Edge: the SAFR Approach—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