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 1426–1450 of 10307 papers

TitleStatusHype
MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia-like Diseases Diagnosis From X-ray ScansCode1
Multi-domain Recommendation with Embedding Disentangling and Domain AlignmentCode1
MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus ImagesCode1
Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic SegmentationCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
Multilingual acoustic word embedding models for processing zero-resource languagesCode1
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-TuningCode1
MultiLoKo: a multilingual local knowledge benchmark for LLMs spanning 31 languagesCode1
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer LearningCode1
Multimodal Side-Tuning for Document ClassificationCode1
Multinational Address Parsing: A Zero-Shot EvaluationCode1
Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamicsCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to BanglaCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic SegmentationCode1
Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image SegmentationCode1
Multi-Task Reinforcement Learning with Context-based RepresentationsCode1
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape RecognitionCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural NetworksCode1
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer LearningCode1
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional FiltersCode1
CLiMB: A Continual Learning Benchmark for Vision-and-Language TasksCode1
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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