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 3301–3350 of 10307 papers

TitleStatusHype
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning—0
DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems—0
Pengi: An Audio Language Model for Audio TasksCode2
Exploring the Viability of Synthetic Query Generation for Relevance Prediction—0
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens—0
Efficient ConvBN Blocks for Transfer Learning and BeyondCode1
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits—0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation—0
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational AbilitiesCode3
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment ClassificationCode0
A Survey on Time-Series Pre-Trained ModelsCode2
Instruction Tuned Models are Quick LearnersCode0
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study—0
AD-KD: Attribution-Driven Knowledge Distillation for Language Model CompressionCode1
One-Prompt to Segment All Medical ImagesCode1
Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers—0
Real-Time Flying Object Detection with YOLOv8Code1
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks—0
Tailoring Instructions to Student's Learning Levels Boosts Knowledge DistillationCode1
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech TranslationCode0
Transfer Learning for Causal Effect Estimation—0
Osteosarcoma Tumor Detection using Transfer Learning Models—0
Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence—0
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images—0
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion—0
Deep Ensembling for Perceptual Image Quality Assessment—0
A Whisper transformer for audio captioning trained with synthetic captions and transfer learningCode1
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks—0
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Meta-models for transfer learning in source localisation—0
Predictive Models from Quantum Computer Benchmarks—0
Towards Automated COVID-19 Presence and Severity Classification—0
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural RepresentationsCode1
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
Cross-Language Transfer Learning using Visual Information for Automatic Sign Gesture Recognition—0
Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN—0
ML-Based Teaching Systems: A Conceptual Framework—0
To transfer or not transfer: Unified transferability metric and analysis—0
Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue GenerationCode0
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN TrainingCode0
Learning representations that are closed-form Monge mapping optimal with application to domain adaptationCode0
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation—0
A Deep Learning-based Compression and Classification Technique for Whole Slide Histopathology Images—0
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation ExtractionCode1
Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classificationCode0
Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval—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