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 8951–9000 of 10307 papers

TitleStatusHype
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans—0
Crossmodal Knowledge Distillation with WordNet-Relaxed Text Embeddings for Robust Image Classification—0
Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR—0
Cross-Modal Knowledge Transfer via Inter-Modal Translation and Alignment for Affect Recognition—0
Cross-Modal Knowledge Transfer Without Task-Relevant Source Data—0
Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing—0
Multilingual Speech Translation with Efficient Finetuning of Pretrained Models—0
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding—0
Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Cross-subject Decoding of Eye Movement Goals from Local Field Potentials—0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces—0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
Cross-Task Knowledge Distillation in Multi-Task Recommendation—0
Cross-Task Knowledge Transfer for Query-Based Text Summarization—0
Cross-Task Knowledge Transfer for Visually-Grounded Navigation—0
Cross-Task Representation Learning for Anatomical Landmark Detection—0
Cross-user activity recognition via temporal relation optimal transport—0
CrossVoice: Crosslingual Prosody Preserving Cascade-S2ST using Transfer Learning—0
CrosswalkNet: An Optimized Deep Learning Framework for Pedestrian Crosswalk Detection in Aerial Images with High-Performance Computing—0
Crowd-Powered Data Mining—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach—0
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers—0
CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets—0
CSG0: Continual Urban Scene Generation with Zero Forgetting—0
CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning—0
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement—0
CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning—0
Cued Speech Generation Leveraging a Pre-trained Audiovisual Text-to-Speech Model—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
CUNI Basque-to-English Submission in IWSLT18—0
CUNI Submission for Low-Resource Languages in WMT News 2019—0
CUNI Submission for the Inuktitut Language in WMT News 2020—0
CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20—0
Curricular Transfer Learning for Sentence Encoded Tasks—0
Curriculum Based Multi-Task Learning for Parkinson's Disease Detection—0
Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability—0
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks—0
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning—0
Customizing Contextualized Language Models forLegal Document Reviews—0
Customizing General-Purpose Foundation Models for Medical Report Generation—0
CVAE-based Re-anchoring for Implicit Discourse Relation Classification—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—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