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 3226–3250 of 10307 papers

TitleStatusHype
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability—0
Multitask learning for recognizing stress and depression in social media—0
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—0
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuningCode0
Multi-source adversarial transfer learning based on similar source domains with local features—0
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity—0
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement LearningCode1
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
Deeply Coupled Cross-Modal Prompt LearningCode1
JutePestDetect: An Intelligent Approach for Jute Pest Identification Using Fine-Tuned Transfer Learning—0
Transfer Learning for Power Outage Detection Task with Limited Training Data—0
Augmenting Character Designers Creativity Using Generative Adversarial Networks—0
One Network, Many Masks: Towards More Parameter-Efficient Transfer LearningCode1
PotatoPestNet: A CTInceptionV3-RS-Based Neural Network for Accurate Identification of Potato Pests—0
Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models—0
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image RecognitionCode1
Parallel Corpus for Indigenous Language Translation: Spanish-Mazatec and Spanish-MixtecCode0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
Matrix Information Theory for Self-Supervised LearningCode1
Vision Transformers for Small Histological Datasets Learned through Knowledge DistillationCode0
GVdoc: Graph-based Visual Document ClassificationCode0
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers—0
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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