# Tag Info

## Hot answers tagged semi-supervised-learning

9

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: Self-Supervised Learning: This line of work aims to learn image representations without requiring human-annotated labels and then use those learned representations on some ...

8

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 ...

7

Semi-supervised learning 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 ...

5

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 ...

4

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) ...

2

In the introduction, the authors write We consider the problem of classifying nodes (such as documents) in a graph (such as a citation network), where labels are only available for a small subset of nodes. This problem can be framed as graph-based semi-supervised learning, where label information is smoothed over the graph via some form of explicit graph-...

1

If you ruled out leakage completely read this observation about double decent https://openai.com/blog/deep-double-descent/ This Blogpost from openAI shares the observation that the validation loss can decrease again even after initially increasing (which is typically a sign for the start of overfitting, e.g. in early stopping).

1

You did a great job at this... You can use the Tensorflow’s LogSumExp built-in function to avoid numerical problems. This routine prevents over/under flow issues that may occur when LogSumExp encounters very extreme, either positive or negative values. You have sorted out this: There need to be Images from the generator. To these ones, the discriminator ...

1

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 ...

1

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....

1

Supervised Pros: highest accuracy Cons: need a large human-labeled training set brittle (doesn't work well with examples that are in a different genre from the training set) Semi-supervised Relation bootstrapping Pros: only requires a small set of labeled data (seed relations) Cons: complex iterative process Distant supervision Pros: training ...

1

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....

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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 ...

1

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, ...

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