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
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
Advancing Transformer's Capabilities in Commonsense ReasoningCode0
TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised LearningCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Continual Dialogue State Tracking via Reason-of-Select DistillationCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Taskonomy: Disentangling Task Transfer LearningCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species GenerationCode0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
A Study of Convolutional Architectures for Handshape Recognition applied to Sign LanguageCode0
A Neural Network based Framework for Effective Laparoscopic Video Quality AssessmentCode0
Encodings for Prediction-based Neural Architecture SearchCode0
End-to-End Deep Learning of Optimization HeuristicsCode0
AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image SegmentationCode0
Contextual Dialogue Act Classification for Open-Domain Conversational AgentsCode0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
Tensor Analysis with n-Mode Generalized Difference SubspaceCode0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer LearningCode0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Context Matters: Leveraging Spatiotemporal Metadata for Semi-Supervised Learning on Remote Sensing ImagesCode0
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to RCode0
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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