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 151–175 of 1356 papers

TitleStatusHype
General Instance Distillation for Object DetectionCode1
An Information-Theoretic Justification for Model PruningCode1
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware TransformationCode1
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture SearchCode1
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement LearningCode1
Show, Attend and Distill:Knowledge Distillation via Attention-based Feature MatchingCode1
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-PointCode1
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient DetectorsCode1
EarlyBERT: Efficient BERT Training via Early-bird Lottery TicketsCode1
Computation-Efficient Knowledge Distillation via Uncertainty-Aware MixupCode1
Neural Pruning via Growing RegularizationCode1
Progressive Network Grafting for Few-Shot Knowledge DistillationCode1
DE-RRD: A Knowledge Distillation Framework for Recommender SystemCode1
Going Beyond Classification Accuracy Metrics in Model CompressionCode1
Multi-level Knowledge Distillation via Knowledge Alignment and CorrelationCode1
KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and QuantizationCode1
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing SystemsCode1
HAWQV3: Dyadic Neural Network QuantizationCode1
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement LearningCode1
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
Passport-aware Normalization for Deep Model ProtectionCode1
CompRess: Self-Supervised Learning by Compressing RepresentationsCode1
Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank DeterminationCode1
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's DistanceCode1
Contrastive Distillation on Intermediate Representations for Language Model CompressionCode1
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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