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 3251–3275 of 10307 papers

TitleStatusHype
Distilling BlackBox to Interpretable models for Efficient Transfer LearningCode1
Live American Sign Language Letter Classification with Convolutional Neural Networks—0
Do We Really Need a Large Number of Visual Prompts?—0
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems—0
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical TasksCode2
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression—0
Collective Knowledge Graph Completion with Mutual Knowledge Distillation—0
Transfer Learning for Personality Perception via Speech Emotion Recognition—0
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text TranslationCode1
TOAST: Transfer Learning via Attention SteeringCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
READ: Recurrent Adaptation of Large Transformers—0
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning—0
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical InsightsCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
Few-shot Unified Question Answering: Tuning Models or Prompts?—0
A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing—0
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine TranslationCode0
Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning—0
Selective Pre-training for Private Fine-tuningCode0
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers—0
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language ModelsCode1
Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing—0
Deep Transductive Transfer Learning for Automatic Target Recognition—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