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 3526–3550 of 10307 papers

TitleStatusHype
Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation—0
Federated Learning without Full Labels: A Survey—0
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning—0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
Decoupled Multimodal Distilling for Emotion RecognitionCode1
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown ObjectsCode1
Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams—0
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition—0
An embedding for EEG signals learned using a triplet loss—0
Compositional Zero-Shot Domain Transfer with Text-to-Text Models—0
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event DomainCode1
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter—0
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters—0
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias—0
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning ProblemsCode0
Exploring the Benefits of Visual Prompting in Differential PrivacyCode0
Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility—0
Generate labeled training data using Prompt Programming and GPT-3. An example of Big Five Personality Classification—0
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions—0
Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks—0
Fix the Noise: Disentangling Source Feature for Controllable Domain TranslationCode1
Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns—0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Manipulating Transfer Learning for Property InferenceCode0
ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked AutoencodersCode0
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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