Inductive Learning
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
The OV A-classifier outputs the confidence score of a sample being an inlier, providing a threshold to detect outliers.
A Semi-Supervised Learning Approach and A New Dataset
While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environments given the limited number of annotated data.
d800149d2f947ad4d64f34668f8b20f6-Paper.pdf
Many instances of algorithmic bias are caused by subpopulation shifts.
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
We show that bringing intermediate layers' representations of two augmented
Learning Recourse on Instance Environment to Enhance Prediction Accuracy
The performance of any supervised learning model depends strongly on the quality of input instances.
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Zhongzheng Ren
But not all unlabeled data are equal. In this paper we study how to use a different weight for every unlabeled example. Manual tuning of all those weights - as done in prior work - is no longer possible.
Supplementary information for: H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
Here we give details to our models, and to the encoding and representation of images and questions used in our models. We used a CNN as input encoder in this task. The last fully connected layer was of size 128 followed by a ReLU nonlinearity (BatchNorm denotes a batch normalization layer [2]). Training examples were generated as described in the main text. Here, an example is one full sequence of image pairs (including random images) and one query image.
