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 3826–3850 of 10307 papers

TitleStatusHype
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer—0
Task-aware Adaptive Learning for Cross-domain Few-shot Learning—0
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
Novel Scenes & Classes: Towards Adaptive Open-set Object DetectionCode1
SSDA: Secure Source-Free Domain AdaptationCode0
Robust Heterogeneous Federated Learning under Data CorruptionCode0
LAC - Latent Action Composition for Skeleton-based Action Segmentation—0
Self-Evolved Dynamic Expansion Model for Task-Free Continual LearningCode0
Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression—0
Improved Knowledge Transfer for Semi-Supervised Domain Adaptation via Trico Training Strategy—0
Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality AssessmentCode0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild—0
Quality Diversity for Visual Pre-Training—0
ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector—0
Weak-Shot Object Detection Through Mutual Knowledge Transfer—0
Guided Recommendation for Model Fine-Tuning—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
Implicit Surface Contrastive Clustering for LiDAR Point Clouds—0
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning—0
COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport—0
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous ClientsCode1
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge TransferCode1
Open-Set Fine-Grained Retrieval via Prompting Vision-Language Evaluator—0
Annealing-Based Label-Transfer Learning for Open World Object DetectionCode1
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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