SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1251–1275 of 10307 papers

TitleStatusHype
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State TrackingCode1
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTrackingCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
A Chinese Corpus for Fine-grained Entity TypingCode1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer LearningCode1
Amplifying Membership Exposure via Data PoisoningCode1
Co-Tuning for Transfer LearningCode1
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGANCode1
FNet: Mixing Tokens with Fourier TransformsCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Like What You Like: Knowledge Distill via Neuron Selectivity TransferCode1
Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation ModelsCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
A Strong and Simple Deep Learning Baseline for BCI MI DecodingCode1
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-trainingCode1
CreoleVal: Multilingual Multitask Benchmarks for CreolesCode1
CrAM: A Compression-Aware MinimizerCode1
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language ModelsCode1
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?Code1
Long-tail Augmented Graph Contrastive Learning for RecommendationCode1
Critical Thinking for Language ModelsCode1
Semantic-Fused Multi-Granularity Cross-City Traffic PredictionCode1
Finetune like you pretrain: Improved finetuning of zero-shot vision modelsCode1
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware ModellingCode1
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified