Questions tagged [objective-functions]

For questions related to the concept of loss (or cost) function in the context of machine learning.

94 questions with no upvoted or accepted answers
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339 views

How should we interpret this figure that relates the perceptron criterion and the hinge loss?

I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.2 Relationship with Support Vector Machines says the following: The perceptron criterion is ...
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31 views

How do weights changes handles during back-propagation when there are unknown labels

I have a question about how weights are updated during back-propagation for some of my samples that have unknown labels (please note, unknown, not missing). The reason they are unknown is because this ...
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78 views

Why is the loss associated with my neural network increasing?

I am currently learning neural networks using data from Touchscreen Input as a Behavioral Biometric. Basically, I am trying to predict "User ID" by training the neural network model shown ...
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34 views

How to choose the new layer and objective function for transfer learning on a neural network?

I have a base model $M$ trained on a data say type 1 for task $T$. Now, I want to update $M$ by applying transfer learning for it to work on data type 2 for the same task $T$. I am very new to AI/ML ...
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56 views

Loss function to minimize the distance between sets

Are there references or links to examples about loss functions "Distance Metrics" which could be used to minimize the distance between two sets for a neural network. More precisely, this ...
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38 views

Enforcing sparsity constraints that make use of spatial contiguity

I have a deep learning network that outputs grayscale image reconstructions. In addition to good reconstruction performance (measured through mean squared error or some other measure like psnr), I ...
3
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1answer
71 views

How to perform binary classification when one class is more predominant than the other?

Assuming we have big $m \times n$ input dataset, with $m \times 1$ output vector. It's a classification problem with only two possible values: either $1$ or $0$. Now, the problem is that almost all ...
3
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1answer
131 views

When to use RMSE as opposed to MSE and vice versa?

I understand that RMSE is just the square root of MSE. Generally, as far as I have seen, people seem to use MSE as a loss function and RMSE for evaluation purposes, since it exactly gives you the ...
3
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0answers
273 views

Understanding log probabilities of actions in the PPO objective

I'm trying to implement the Proximal Policy Optimization (PPO) algorithm (code here), but I am confused about certain concepts. What is the correct way to implement log probability of a policy (...
3
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1answer
331 views

Extend the loss function from the single action to the n-action case per time step

My question concerns a side question (which was not answered) asked here: How can policy gradients be applied in the case of multiple continuous actions? I am trying to implement a simple policy ...
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10 views

Can a GIoU loss (generalized intersection over union) be used after an STN module (spatial transformer network)?

I have a model that uses an STN module for number detection and Mean Squared Error loss. But I would like to replace it for GIoU, because MSE doesn't take into account how much of the target area has ...
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33 views

Is optimizing weighted sum multi objective tasks considered a multi-task learning?

I have two sequence prediction tasks, finding $\vec{\pi} \in \Pi$ and $\vec{\psi} \in \Psi$. Each sequence has its own objective function, i.e. $f_1(\vec{\pi})$ and $f_2(\vec{\psi})$. The input for ...
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30 views

Is the formula $\frac {1}{s}\sum _{j=1}^{s}|d_{j}-y_{j}(t)|$ the correct form of 0-1 loss function, in the context of Perceptron?

Per page 7 of this MIT lecture notes, the original single-layer Perceptron uses 0-1 loss function. Wikipedia uses $${\displaystyle {\frac {1}{s}}\sum _{j=1}^{s}|d_{j}-y_{j}(t)|} \tag{1}$$ to denote ...
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36 views

Could the inputs of the mean squared-error loss function be transformed to allow larger learning rates?

In the context of a neural network $\hat{y} = f_\theta(\mathbf{x})$ with parameters $\theta$ that is trained to perform regression such that the prediction $\hat{\mathbf{y}} = [\hat{y}_1,\hat{y}_2,...,...
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19 views

Is it normal getting noise values in the error history along training iteration?

I'm giving my first steps in really learning machine learning. As an exercise in my online course, it was asked for me to code the Cost function of some neural network that should resolve the ...
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32 views

Single-value loss/training in a CNN with a tensor output

I am playing around with an idea of using using Q-learning with a DQN (Deep Q-Network), to determine the optimal position of a number of 'units' on a grid of allowed locations, according to some ...
2
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0answers
30 views

Loss function decays linearly in segmentation MRI fascia

I am working on a segmentation of MRI images of the thigh. I am trying to segment the fascia, there is a slight imbalance between the background and the mask. I have about 1400 images from 30 patients ...
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155 views

What is the purpose of the DAMSM loss for the generators in AttnGAN?

I am confused about the training part in AttnGan. If you observe page 3. There are two types of losses for generator network: one involving the Deep Attentional Multimodal Similarity Model (DAMSM) ...
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42 views

Why would the loss increase on a single fixed input?

I'm training a neural network on some input data. I know that loss increasing may be related to: overfitting, if the loss increases on test data (while still decreases on training data) oscillations ...
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79 views

How can the target rely on untrained parameters?

I'm trying to understand DQN. I understand where the loss function comes from. I'm just unsure about why the target function works in practice. Given the loss function $$ L_i(\theta_i) = [(y_i - Q(s,a;...
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38 views

Incorporating domain knowledge into recurrent network

I am currently trying to solve a classification task with a recurrent artificial neural network (RNN). Situation There are up to 350 inputs (X) mapped on one categorical output (y)(13 differnt ...
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35 views

Single label classification into hierarchical categories using a neural network

I am working on a classification problem into progressive classes. In other words, there is some hierarchy of categories in such a way, that A < B < C, e.g. low, medium, high, very high. What ...
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50 views

Is Mean Squared Error Loss function a good loss function for continuous variables $0 < x < 1$

Suppose I am utilising a neural network to predict the next state, $s'$ based on the current $(s, a)$ pairs. all my neural network inputs are between 0 and 1 and the loss function for this network ...
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31 views

Is there any wrong in my focal loss derivation?

Assume $\mathbf{X} \in R^{N, C}$ is the input of the softmax $\mathbf{P} \in R^{N, C}$, where $N$ is number of examples and $C$ is number of classes: $$\mathbf{p}_i = \left[ \frac{e^{x_{ik}}}{\sum_{j=...
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96 views

Why does GAN loss converge to log(2) and not -log(2)?

In Goodfellow's paper, he says: Hence, by inspecting Eq. 4 at $D^*_G (\mathbf{x}) = \frac{1}{2}$, we find $C(G) = \log \frac{1}{2}+ \log \frac{1}{2} = − \log 4$. To see that this is the best ...
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39 views

Tversky Loss paper implementation: Recall/Precision do not improve as stated

I have been trying to implement this paper and I am very much intrigued. I am working on a medical image problem where I have to segment very small specimens on Whole Slide Images (gigapixel ...
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42 views

How to understand my CNN's training results?

I created a multi-label classification CNN to classify chest X-ray images into zero or more possible lung diseases. I've been doing some configuration tests on it and analyzing its results and I'm ...
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0answers
14 views

Loss function for increasing the quality of the image when labels are not perfectly alligned

I am trying to increse the quality of the images that I gather from the microscope. That is a acoustic microscope and there are lots of technical details but in a nutshell the low quality images and ...
2
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0answers
55 views

How to implement loss function of H-GAN model

I was trying to implement the loss function of H-GAN. Here is my code . But it seem somethings wrong, maybe is recognition loss on z (EQ 9). I used the EQ 5 on MISO to calculate it. Here is my code: ...
2
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2answers
48 views

Maximize loss on non-target variable

I have a neural network that should be able to classify documents to target label A. The problem is that the network is actually classifying label B, which is an easier task. To make the problem more ...
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0answers
96 views

Is it possible to use Reward Function of type R(s, a, s') if more than one action is applied?

I am applying a reinforcement learning agent (PPO2, stable baselines implementation) to a custom built environment using OpenAI Gym. One reward function (formualted as loss function, that is, all ...
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0answers
58 views

Which loss functions for transforming a density function to another density function?

I am looking at a problem which can be distilled as follows: I have a phenomenon which can be modeled as a probability density function which is "messy" in that it sums to unity over its support but ...
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0answers
59 views

Should I use the hyperbolic distance loss in the case of Poincarè Disk Model?

I trained a neural network which makes a regression to a Poincarè Disk Model with radius $r = 1$. I want to optimize using the hyperbolic distance $$ \operatorname{arcosh} \left( 1 + \frac{2|pq|^2|...
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45 views

What is the confusion loss for adversarial learning?

What is the confusion loss used in domain adaptation (DA) for adversarial learning/GANs? See this paper. Two domains: $s$: source domain $t$: target domain Generator/Discriminator setting: $M_s:x_s\...
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31 views

When can we call a loss function "adaptive"?

A loss function is a measure of how bad our neural network is. We can decrease the loss by proper training. I came across the phrase "adaptive loss function" in several research papers. For ...
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25 views

Can people set loss function of neural network by themselves instead of choosing cross entropy or mean square error?

I found people used deep neural network to get optimal policy by solving a nonconvex optimization problem. Moreover, they didn't use any set of training data and claimed that it's the difference ...
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18 views

Do dataset sizes matter in a Style GAN?

When working with classifiers, a class imbalance is a huge issue for our models. If we have too many images of class 1 and too few images from ...
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13 views

How to update all the weights in case only one data out of n signals is observable

If we have cost function as $$E_i = (D_i -Y_i)^T Q (D_i -Y_i)$$, where $$Q=\begin{bmatrix} 1 & 0 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0 \end{bmatrix}$$( in case only one data signal can ...
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60 views

BlackOut - ICLR 2016: need help understanding the cost function derivative

In the ICLR 2016 paper BlackOut: Speeding up Recurrent Neural Network Language Models with very Large Vocabularies, on page 3, for eq. 4: $$ J_{ml}^s(\theta) = log \ p_{\theta}(w_i | s) $$ They have ...
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24 views
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60 views

How to make SAC (Soft-Actor-Critic) learn a policy?

I cannot make SAC learn a task in a certain environment. The point is that it actually sometimes finds a very good policy, but it never learns the policy in the end. I am using the SAC implementation ...
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24 views

What is the intuition behind equations 10, 11 and 12 of the paper "Noise2Noise: Learning Image Restoration without Clean Data"?

Can anyone help me understand these functions described in the paper Noise2Noise: Learning Image Restoration without Clean Data I have read the portion A.4 in the appendix but need a more detailed and ...
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1answer
77 views

Explanation of this L2 minimization equation

I am trying to understand the last two lines of this math notation (from this paper). How did Var and double summation of Cov come to the equation? The first two lines I understood something like $(a-...
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1answer
107 views

Explain the difference in graphical patterns between discriminator fake loss and generator loss in GAN

In GAN (generative adversarial networks), let us take "binary cross-entropy" as the loss function for discriminator $$(overall \; loss = -\sum log(D(x_i)) -\sum log(1-D(G(z_i))) $$ $$ where \...
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31 views

What is a "center loss"?

I have seen that a center loss is beneficial in computer vision, especially in face recognition. I have tried to understand this concept from the following material A Discriminative Feature Learning ...
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28 views

Could the data augmentation lead to the model learning features which corresponds to data augmented data and not to the real data?

I am trying to train a Unet network with Synthetic data to do binary segmentation due to the fact that is is not easy to collect real data. And there is something in the training process that I do not ...
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0answers
29 views

How to find distance between 2 points when dimensions are all of different nature?

I have a dataset with four features: the x coordinate the y coordinate the velocity magnitude angle Now, I want to measure the distance between two points in the dataset, taking into account the ...
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94 views

Which loss function to choose for imbalanced datasets?

For imbalanced datasets (either in the context of computer vision or NLP), from what I learned, it is good to use a weighted log loss. However, in competitions, the people who are in top positions are ...
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165 views

Classification or regression for deep Q learning

DQN implemented at https://github.com/PacktPublishing/PyTorch-1.x-Reinforcement-Learning-Cookbook/blob/master/Chapter07/chapter7/dqn.py uses the mean square error loss function for the neural network ...
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0answers
59 views

Loss Function In Units Of Bits?

Where can I find a machine learning library that implements loss functions measuring the Algorithmic Information Theoretic-friendly quantity "bits of ...