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 4551–4600 of 10307 papers

TitleStatusHype
A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service InferenceCode0
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
Gaze-based Attention Recognition for Human-Robot Collaboration—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation—0
Towards Understanding the Effect of Pretraining Label Granularity—0
Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets—0
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning—0
Generalizable Local Feature Pre-training for Deformable Shape AnalysisCode0
Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces—0
Δ-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss—0
Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review—0
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing—0
Guided Transfer LearningCode0
Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation—0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks—0
Federated Learning without Full Labels: A Survey—0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection—0
Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams—0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning—0
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition—0
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias—0
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter—0
Compositional Zero-Shot Domain Transfer with Text-to-Text Models—0
An embedding for EEG signals learned using a triplet loss—0
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters—0
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning ProblemsCode0
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
Exploring the Benefits of Visual Prompting in Differential PrivacyCode0
Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility—0
Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks—0
Continual Learning in the Presence of Spurious Correlation—0
Manipulating Transfer Learning for Property InferenceCode0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns—0
ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked AutoencodersCode0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
A model is worth tens of thousands of examples—0
Transfer learning method in the problem of binary classification of chest X-raysCode0
LION: Implicit Vision Prompt Tuning—0
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models—0
Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language—0
Efficient Computation Sharing for Multi-Task Visual Scene UnderstandingCode0
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment ClassificationCode0
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models—0
Imitation and Transfer Learning for LQG Control—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