Unsupervised or Indirectly Supervised Learning
Return of Unconditional Generation: A Self-supervised Representation Generation Method
Unconditional generation--the problem of modeling data distribution without relying on human-annotated labels--is a long-standing and fundamental challenge in generative models, creating a potential of learning from large-scale unlabeled data. In the literature, the generation quality of an unconditional method has been much worse than that of its conditional counterpart. This gap can be attributed to the lack of semantic information provided by labels. In this work, we show that one can close this gap by generating semantic representations in the representation space produced by a self-supervised encoder. These representations can be used to condition the image generator.
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation Liyao T ang
This approach may, however, hinder the comprehensive exploitation of unlabeled data points. We hypothesize that this selective usage arises from the noise in pseudo-labels generated on unlabeled data. The noise in pseudo-labels may result in significant discrepancies between pseudo-labels and model predictions, thus confusing and affecting the model training greatly.
Granger Components Analysis: Unsupervised learning of latent temporal dependencies
Here the concept of Granger causality is employed to propose a new criterion for unsupervised learning that is appropriate in the case of temporally-dependent source signals. The basic idea is to identify two projections of a multivariate time series such that the Granger causality among the resulting pair of components is maximized.
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances.
Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability
The continued scaling of flash memory technology into smaller process nodes, combined with the increased information capacity of each flash cell (i.e, storing more bits per cell), has placed NAND flash memory at the forefront of modern storage technology.
Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
Under this setting, previous SSL methods tend to predict wrong pseudo-labels with the model fitted on labeled data, resulting in noise accumulation.
The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
However, zero-shot learning methods still require human instruction set to solve a new task.
Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning Seonghyun Ban
Previous approaches often downweight instances from irrelevant classes to mitigate the negative impact of class distribution mismatch on model training.
A Novel Approach for Eff ective Multi-View Clustering with Information-Theoretic Perspective
Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources.