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 7176–7200 of 10307 papers

TitleStatusHype
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces—0
Building Height Prediction with Instance Segmentation—0
Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition—0
TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning—0
TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning—0
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems—0
Tag that issue: Applying API-domain labels in issue tracking systems—0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
Taiwanese-Accented Mandarin and English Multi-Speaker Talking-Face Synthesis System—0
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation—0
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection—0
Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions—0
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication—0
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces—0
Tao: Re-Thinking DL-based Microarchitecture Simulation—0
Building medical image classifiers with very limited data using segmentation networks—0
TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality—0
Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures—0
Target Aware Network Adaptation for Efficient Representation Learning—0
Targeted Attention for Generalized- and Zero-Shot Learning—0
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach—0
Target PCA: Transfer Learning Large Dimensional Panel Data—0
Target Speaker Lipreading by Audio-Visual Self-Distillation Pretraining and Speaker Adaptation—0
Target Transfer Q-Learning and Its Convergence Analysis—0
TartuNLP at EvaLatin 2024: Emotion Polarity Detection—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