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 3376–3400 of 10307 papers

TitleStatusHype
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
Detect, Distill and Update: Detect, Distill and Update: Learned DB Systems Facing Out of Distribution DataCode0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
Deception Detection with Feature-Augmentation by soft Domain Transfer—0
Towards Unbiased Training in Federated Open-world Semi-supervised Learning—0
Procedural Content Generation via Knowledge Transformation (PCG-KT)—0
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on GraphsCode0
Redundancy and Concept Analysis for Code-trained Language Models—0
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and EquityCode0
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings—0
Transfer of knowledge among instruments in automatic music transcription—0
The ART of Transfer Learning: An Adaptive and Robust Pipeline—0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization—0
Limits of Model Selection under Transfer Learning—0
Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge—0
Polyp-SAM: Transfer SAM for Polyp SegmentationCode1
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis—0
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRCode0
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification—0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Synergy of Machine and Deep Learning Models for Multi-Painter RecognitionCode0
BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text—0
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization—0
Lightweight, Pre-trained Transformers for Remote Sensing TimeseriesCode2
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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