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 3401–3425 of 10307 papers

TitleStatusHype
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance LearningCode0
Contextual Dialogue Act Classification for Open-Domain Conversational AgentsCode0
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Causally Abstracted Multi-armed BanditsCode0
Efficient Transfer Learning for Video-language Foundation ModelsCode0
Enhancing Dataset Distillation via Non-Critical Region RefinementCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
Context Matters: Leveraging Spatiotemporal Metadata for Semi-Supervised Learning on Remote Sensing ImagesCode0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
End-to-End Deep Learning of Optimization HeuristicsCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
eGAN: Unsupervised approach to class imbalance using transfer learningCode0
Encodings for Prediction-based Neural Architecture SearchCode0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian TwitterCode0
Elastic Coupled Co-clustering for Single-Cell Genomic DataCode0
A Neural Network based Framework for Effective Laparoscopic Video Quality AssessmentCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Enhancing textual textbook question answering with large language models and retrieval augmented generationCode0
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled DataCode0
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural NetworksCode0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif DiscoveryCode0
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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