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 5301–5325 of 10307 papers

TitleStatusHype
Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks—0
Revisiting Gradient Episodic Memory for Continual Learning—0
Revisiting knowledge transfer for training object class detectors—0
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning—0
Revisiting MLLMs: An In-Depth Analysis of Image Classification Abilities—0
Revisiting Pretraining with Adapters—0
Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings—0
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer—0
Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition—0
Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm—0
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning—0
Reward-Aware Proto-Representations in Reinforcement Learning—0
Rewards-based image analysis in microscopy—0
Reweighted Proximal Pruning for Large-Scale Language Representation—0
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models—0
RF-GML: Reference-Free Generative Machine Listener—0
RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning—0
Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures—0
Ridesourcing Car Detection by Transfer Learning—0
Riemannian Geometry-Based EEG Approaches: A Literature Review—0
RIPEx: Extracting malicious IP addresses from security forums using cross-forum learning—0
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health—0
Risk-Aware Transfer in Reinforcement Learning using Successor Features—0
Adaptive Deep Learning for Entity Resolution by Risk Analysis—0
Risk of Transfer Learning and its Applications in Finance—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