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

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Showing 101–125 of 1356 papers

TitleStatusHype
Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer InferenceCode1
An Empirical Study of CLIP for Text-based Person SearchCode1
CrossKD: Cross-Head Knowledge Distillation for Object DetectionCode1
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing SystemsCode1
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN ExecutionCode1
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language UnderstandingCode1
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's DistanceCode1
An Information Theory-inspired Strategy for Automatic Network PruningCode1
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient DetectorsCode1
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-PointCode1
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model CompressionCode1
Data-Free Network Quantization With Adversarial Knowledge DistillationCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
EarlyBERT: Efficient BERT Training via Early-bird Lottery TicketsCode1
Densely Guided Knowledge Distillation using Multiple Teacher AssistantsCode1
Differentiable Model Compression via Pseudo Quantization NoiseCode1
DE-RRD: A Knowledge Distillation Framework for Recommender SystemCode1
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image RetrievalCode1
3DG-STFM: 3D Geometric Guided Student-Teacher Feature MatchingCode1
Efficient On-Device Session-Based RecommendationCode1
AD-KD: Attribution-Driven Knowledge Distillation for Language Model CompressionCode1
A Real-time Low-cost Artificial Intelligence System for Autonomous Spraying in Palm PlantationsCode1
Enhancing Cross-Tokenizer Knowledge Distillation with Contextual Dynamical MappingCode1
Discrimination-aware Network Pruning for Deep Model CompressionCode1
Dual Relation Knowledge Distillation for Object DetectionCode1
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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