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 1451–1475 of 10307 papers

TitleStatusHype
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imageryCode1
Neural Topic Modeling with Continual Lifelong LearningCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
CLiMB: A Continual Learning Benchmark for Vision-and-Language TasksCode1
Novel Class Discovery for Ultra-Fine-Grained Visual CategorizationCode1
n-Reference Transfer Learning for Saliency PredictionCode1
n-Reference Transfer Learning for Saliency PredictionCode1
An Empirical Study on Cross-X Transfer for Legal Judgment PredictionCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot LabelsCode1
OceanBench: The Sea Surface Height EditionCode1
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?Code1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
CLIP-Lite: Information Efficient Visual Representation Learning with Language SupervisionCode1
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question AnsweringCode1
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?Code1
One Network, Many Masks: Towards More Parameter-Efficient Transfer LearningCode1
One-stage Low-resolution Text Recognition with High-resolution Knowledge TransferCode1
On Latency Predictors for Neural Architecture SearchCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
On the effectiveness of task granularity for transfer learningCode1
Continual Prompt Tuning for Dialog State TrackingCode1
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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