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Found 6 papers, 4 papers with code
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled source data.
CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks
We also present a pipeline that combines Image Processing and Deep Learning models.
360FusionNeRF: Panoramic Neural Radiance Fields with Joint Guidance
Additionally, we introduce a semantic consistency loss that encourages realistic renderings of novel views.
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Active Data Manipulation
Federated learning (FL) has recently emerged as a privacy-preserving approach for machine learning in domains that rely on user interactions, particularly recommender systems (RS) and online learning to rank (OLTR).
CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients
The healthcare landscape is evolving, with patients seeking more reliable information about their health conditions, treatment options, and potential risks.
V-FLUTE: Visual Figurative Language Understanding with Textual Explanations
We frame the visual figurative language understanding problem as an explainable visual entailment task, where the model has to predict whether the image (premise) entails a claim (hypothesis) and justify the predicted label with a textual explanation.
