domain classification
36 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in domain classification
Most implemented papers
PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph Learning
qcri/pdns-net
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Furthermore, the latter graphs are small in size rendering them insufficient to understand how graph learning algorithms perform in terms of classification metrics and computational resource utilization.
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
Fei-Long121/DeepBDC
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CVPR 2022
Few-shot classification is a challenging problem as only very few training examples are given for each new task.
Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification
gokling1219/UM2L
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IEEE Transactions on Image Processing 2022
However, the existing methods based on meta learning still need to construct a labeled source data set with several pre-collected HSIs, and must utilize a large number of labeled samples for meta-training, which is actually time-consuming and labor-intensive.
GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling
Finally, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked with a minimum number of annotations ($\sim$ 15).
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
zwenyu/lccs
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Recent test-time adaptation methods update batch normalization layers of pre-trained source models deployed in new target environments with streaming data to mitigate such performance degradation.
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
trinhvg/impash
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The appearance of histopathology images depends on tissue type, staining and digitization procedure.
Back-to-Bones: Rediscovering the Role of Backbones in Domain Generalization
Domain Generalization (DG) studies the capability of a deep learning model to generalize to out-of-training distributions.
GLeaD: Improving GANs with A Generator-Leading Task
Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition.
Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification
The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice.
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
yuweiyin/ttida
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In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data.
