Semi-supervised learning is the collection of machine learning techniques where there are two datasets: a labelled one and an unlabelled one.
There are two main problems that can be solved using semi-supervised learning:
transductive learning (i.e. label the given unlabelled data) and
inductive learning (generalization) (i.e. find a ...
According to Galstyan and Cohen (2007), a hard label is a label assigned to a member of a class where membership is binary: either the element in question is a member of the class (has the label), or it is not.
A soft label is one which has a score (probability or likelihood) attached to it. So the element is a member of the class in question with ...
Both semi-supervised and self-supervised methods are similar in the sense that the goal is to learn with fewer labels per class. The way both formulate this is quite different:
This line of work aims to learn image representations without requiring human-annotated labels and then use those learned representations on some ...
The previous answer has given a good insight into the difference between two areas. I would like to give more examples.
Semi-Supervised Learning work with improving the data set by adding up new examples. There are iterative systems where we train a model on a given dataset and improve the model further after deploying it on the real world by adding ...
Sorry cannot directly reply to your comment as I posted without an account, and you were right! I replaced transposed layers with Upscale1D+Conv1D and that solved the issue.
gen = Conv1DTranspose(128, 4, strides=2, padding='same', kernel_initializer=w_init, use_bias=None)(gen)
should become (notice that strides=2 becomes strides=1):
gen = Upscale1D()(gen)
The neural network will learn what we teach it, for example with that image only, after finish training, your model will hard to recognize humans with dark skin, glasses, big eyes, etc, the features that two annotated targets don't have.
If your data is big enough, and contain all the feature of humans face, the result should be good.
If not, I recommend a ...
I think the choice of technique strongly depends on how fine-grained your forecast-predictions need to be.
When it comes to forecasting by Reinforcement Learning (RL), one prominent example is the stock-trading RL agent. The agent must decide which stock to buy or sell, thereby drawing upon predictions concerting the expected future development of some stock....
need a large human-labeled training set
brittle (doesn't work well with examples that are in a different genre from the training set)
only requires a small set of labeled data (seed relations)
complex iterative process
Contrastive learning is a framework that learns similar/dissimilar representations from data that are organized into similar/dissimilar pairs. This can be formulated as a dictionary look-up problem.
If I conceptually compare the loss mechanisms for:
SimCLR: A Simple Framework for Contrastive Learning of Visual Representations @ https://arxiv.org/abs/2002....
The authors of your cited paper use the term graph-based semi-supervised learning (G-SSL) to refer to semi-supervised learning techniques which take graph structured data as their input.
Given their main example, the MNIST dataset, is not graph structured, they detail a method for converting the raw Euclidean data $X$ into said form (represented by its ...
so assuming your not allowed to use transfer methodologies (like take an already exisiting elephant object detector) my recommendation is to train a CNN classifier (labels are binary-- elephant exist, elephant doesnt exist) and then use strategies founded in like grad cam. Note there does exist a gradcam++ but because you can assure theres only one instance, ...