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

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Showing 1176–1200 of 10307 papers

TitleStatusHype
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modelingCode1
WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management TasksCode1
DREAM+: Efficient Dataset Distillation by Bidirectional Representative MatchingCode1
Drug and Disease Interpretation Learning with Biomedical Entity Representation TransformerCode1
DTL: Disentangled Transfer Learning for Visual RecognitionCode1
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learningCode1
3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer LearningCode1
Dynamic Domain Adaptation for Efficient InferenceCode1
A Study of Face Obfuscation in ImageNetCode1
AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder NetworksCode1
EEG Channel Interpolation Using Deep Encoder-decoder NetwoksCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
EEV: A Large-Scale Dataset for Studying Evoked Expressions from VideoCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
A Chinese Corpus for Fine-grained Entity TypingCode1
Benchmarking and scaling of deep learning models for land cover image classificationCode1
Audio-based Near-Duplicate Video Retrieval with Audio Similarity LearningCode1
Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of AdaptersCode1
Calibration-free online test-time adaptation for electroencephalography motor imagery decodingCode1
Efficient Training of Large Vision Models via Advanced Automated Progressive LearningCode1
Learning Efficient Vision Transformers via Fine-Grained Manifold DistillationCode1
Domain Consistency Representation Learning for Lifelong Person Re-IdentificationCode1
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural NetworksCode1
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate DomainsCode1
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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