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 76–100 of 1356 papers

TitleStatusHype
Efficient and Robust Quantization-aware Training via Adaptive Coreset SelectionCode1
MobileNMT: Enabling Translation in 15MB and 30msCode1
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-TuningCode1
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision ModelsCode1
An Efficient Multilingual Language Model Compression through Vocabulary TrimmingCode1
AD-KD: Attribution-Driven Knowledge Distillation for Language Model CompressionCode1
Class Attention Transfer Based Knowledge DistillationCode1
Performance-aware Approximation of Global Channel Pruning for Multitask CNNsCode1
The Tiny Time-series Transformer: Low-latency High-throughput Classification of Astronomical Transients using Deep Model CompressionCode1
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and UnderstandingCode1
Dual Relation Knowledge Distillation for Object DetectionCode1
UPop: Unified and Progressive Pruning for Compressing Vision-Language TransformersCode1
Compression-Aware Video Super-ResolutionCode1
FFNeRV: Flow-Guided Frame-Wise Neural Representations for VideosCode1
RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision TransformersCode1
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street ViewsCode1
Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank AutoregressionCode1
Unbiased Knowledge Distillation for RecommendationCode1
Sparse Probabilistic Circuits via Pruning and GrowingCode1
Parameter-Efficient Masking NetworksCode1
Less is More: Task-aware Layer-wise Distillation for Language Model CompressionCode1
Basic Binary Convolution Unit for Binarized Image Restoration NetworkCode1
Efficient On-Device Session-Based RecommendationCode1
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision TransformersCode1
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model GeneralizationCode1
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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