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 9001–9025 of 10307 papers

TitleStatusHype
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer LearningCode0
Combined Reinforcement Learning via Abstract RepresentationsCode0
Combining datasets to increase the number of samples and improve model fittingCode0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context LearningCode0
Common Representation Learning Using Step-based Correlation Multi-Modal CNNCode0
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging ClassificationCode0
Comparative Analysis of Pretrained Audio Representations in Music Recommender SystemsCode0
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative StudyCode0
Comparative Analysis: Violence Recognition from Videos using Transfer LearningCode0
Comparative evaluation of CNN architectures for Image Caption GenerationCode0
Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approachCode0
Complete CVDL Methodology for Investigating Hydrodynamic InstabilitiesCode0
Completing Single-Cell DNA Methylome Profiles via Transfer Learning Together With KL-DivergenceCode0
Component Transfer Learning for Deep RL Based on Abstract RepresentationsCode0
Compression Artifacts Reduction by a Deep Convolutional NetworkCode0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
Truly Generalizable Radiograph Segmentation with Conditional Domain AdaptationCode0
Conditional Prototype Rectification Prompt LearningCode0
CoNet: Collaborative Cross Networks for Cross-Domain RecommendationCode0
Consensus Focus for Object Detection and minority classesCode0
Constrained Decoding for Cross-lingual Label ProjectionCode0
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural NetworksCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
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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