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 651–675 of 1356 papers

TitleStatusHype
Language model compression with weighted low-rank factorization—0
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach—0
Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification—0
An Automatic and Efficient BERT Pruning for Edge AI Systems—0
Knowledge Distillation for Oriented Object Detection on Aerial Images—0
Revisiting Self-Distillation—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks—0
STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor DecompositionCode0
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation—0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask—0
DiSparse: Disentangled Sparsification for Multitask Model CompressionCode1
Differentially Private Model Compression—0
Canonical convolutional neural networksCode0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate—0
RLx2: Training a Sparse Deep Reinforcement Learning Model from ScratchCode1
Towards Efficient 3D Object Detection with Knowledge DistillationCode1
MiniDisc: Minimal Distillation Schedule for Language Model CompressionCode0
Do we need Label Regularization to Fine-tune Pre-trained Language Models?—0
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models—0
PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D DetectionCode1
Aligning Logits Generatively for Principled Black-Box Knowledge DistillationCode0
InDistill: Information flow-preserving knowledge distillation for model compressionCode0
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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