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 4301–4350 of 10307 papers

TitleStatusHype
Enabling Continual Learning in Neural Networks with Meta Learning—0
Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Emulation Learning for Neuromimetic Systems—0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
A Petri Dish for Histopathology Image Analysis—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)—0
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture—0
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification—0
Challenges in including extra-linguistic context in pre-trained language models—0
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings—0
Employing High-Dimensional RIS Information for RIS-aided Localization Systems—0
Challenges for cognitive decoding using deep learning methods—0
A PAC-Bayesian bound for Lifelong Learning—0
Employing Federated Learning for Training Autonomous HVAC Systems—0
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation—0
Empirically Measuring Transfer Distance for System Design and Operation—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Empirical Gaussian priors for cross-lingual transfer learning—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling—0
Adversarial Inductive Transfer Learning with input and output space adaptation—0
Action Recognition for American Sign Language—0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning—0
Assessing the Performance of Analog Training for Transfer Learning—0
Emotion Recognition Using Fusion of Audio and Video Features—0
Conversational Transfer Learning for Emotion Recognition—0
Emotion Detection from EEG using Transfer Learning—0
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing—0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
Emotion Classification in Short English Texts using Deep Learning Techniques—0
Emotion Classification in Low and Moderate Resource Languages—0
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network—0
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis—0
EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals—0
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks—0
Any-Shot Sequential Anomaly Detection in Surveillance Videos—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Embodied Multimodal Multitask Learning—0
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces—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