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Questions tagged [incremental-learning]

For questions related to incremental learning algorithms, which are algorithms that attempt to learn new information without forgetting all the previously learned one. Incremental learning is often a synonym for continual (or continuous) learning and lifelong learning.

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Ways to train a neural network continuosly as new data is added [duplicate]

There is a project I'm currently working on that requires object detection with continuous training. The idea is to train a model beforehand with a standard dataset. When I get new images I want to &...
Pedro Carvalho's user avatar
1 vote
1 answer
31 views

Can I do incremental learning with different loss function in neural networks?

I have a saved tensorflow neural network model. I was wondering if it's possible to incrementally train the model but with different nt loss function.
SUNITA GUPTA's user avatar
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0 answers
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Output Not Changing - Feeding Previous Outputs Back Into a Model

Full disclosure, I also posted this on Stack Overflow I have put a more theory based bent towards the question itself here I have a simple model in pytorch based on the quickstart except instead of a ...
cgbsu's user avatar
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0 answers
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What is the detailed experimental setup for class-incremental continual image generation?

Do you condition the generative model (let's say, VAE) on the task identity or the class label or both? If I condition the VAE on both task identity and class label, then I have to provide both the ...
Homie98's user avatar
0 votes
1 answer
348 views

How can I adapt a trained neural network model to learn from newer data containing additional features?

We shall assume that we have a trained neural network model for some task $A$. The dataset used to train the model contained some $n$ features per sample. Using this dataset, we were able to train a ...
user52084's user avatar
0 votes
0 answers
32 views

Is there a way to adapt Particle Swarm Optimization to an incremental/online learning setting?

As stated in the title, is there a way to adapt PSO to an online scenario where new data samples arrive continuously? In more detail: suppose that I have a classifier with several parameters for which ...
Elise Le's user avatar
2 votes
2 answers
190 views

How will MLOps and lifelong learning be complementary?

According to [1], in MLOps, continuous training is a new property, unique to ML systems, that's concerned with automatically retraining and serving the models. While lifelong/incremental learning ...
Lerner Zhang's user avatar
1 vote
1 answer
124 views

Do other online/incremental algorithms not suffer from catastrophic forgetting?

All the literature I read seems to indicate catastrophic forgetting affects only neural networks. Do other online/incremental algorithms not suffer from catastrophic forgetting (for example, ...
user3631804's user avatar
2 votes
0 answers
96 views

Why isn't RL considered a continual learning strategy itself?

I have read about methods that apply continual learning strategies to reinforcement learning. Since reinforcement learning also learns step by step (i.e., task by task, in a sense) during the training ...
convaldo's user avatar
  • 121
2 votes
1 answer
66 views

How to improve a trained model over time (i.e. with more predictions)?

I built a model using the tutorial on the TensorFlow site. It was a simple image classification neural network. I trained it and saved the model and weights together on a ...
Rutvik Karupothula's user avatar
0 votes
1 answer
102 views

How to train a policy model incrementally to solve a problem similar to the vehicle routing problem?

I have a problem similar to the vehicle routing problem (VRP) that I want to solve with reinforcement learning. In this problem, the agent starts from the point $(x_0, y_0)$, then it needs to travel ...
Dan D.'s user avatar
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0 votes
0 answers
79 views

What are good techniques for continuous learning in production?

I was wondering which AI techniques and architectures are used in environments that need predictions to continually improve by the feedback of the user. So let's take some kind of recommendation ...
convaldo's user avatar
  • 121
2 votes
1 answer
1k views

How does a neural network that has been trained keep learning while in a real world scenario

Say I trained a Neural Network (not RNN or CNN) to classify a particular data set. So I train using a specific data set & then I test using another and get an accuracy of 95% which is good enough. ...
user3536523's user avatar
4 votes
1 answer
1k views

Is continuous learning possible with a deep convolutional neural network, without changing its topology?

In general, is continuous learning possible with a deep convolutional neural network, without changing its topology? In my case, I want to use a convolutional neural network as a classifier of ...
Dominiksr's user avatar
3 votes
1 answer
173 views

Is batch learning with gradient descent equivalent to "rehearsal" in incremental learning?

I am learning about incremental learning and read that rehearsal learning is retraining with old data. In essence, isn't this the exact same thing as batch learning (with stochastic gradient descent)? ...
JobHunter69's user avatar
1 vote
1 answer
75 views

Is there any real-time computer vision system that can learn to detect new objects of new classes?

Suppose you have a ground plane and can use a stereo vision system to detect things that are possibly separate objects. Suppose also your robot or agent can attempt to pick up and move these objects ...
FourierFlux's user avatar
1 vote
0 answers
85 views

Why are the current means and the old ones the same in this implementation of Elastic Weight Consolidation?

I'm trying to re-implement Elastic Weight Consolidation (EWC) as outlined in this paper. As a reference, I am also using this Github repository (another implementation). My model/idea is pretty ...
Martin's user avatar
  • 111
3 votes
1 answer
693 views

Transfer learning to train only for a new class while not affecting the predictions of the other class

I am basically interested in vehicle on the road. YoloV3 pytorch is giving a decent result. So my interested Vehicles Car ...
Santhosh Dhaipule Chandrakanth's user avatar
6 votes
3 answers
990 views

What are the state-of-the-art approaches for continual learning with neural networks?

There seems to be a lot of literature and research on the problems of stochastic gradient descent and catastrophic forgetting, but I can't find much on solutions to perform continual learning with ...
gcorso's user avatar
  • 366
2 votes
1 answer
98 views

What are the most common methods to enable neural networks to adapt to changing environments?

For real applications, concept drifts often exist, i.e., the relationship between the input and output changes overtime. Thus, we need our AI or machine learning system to quickly adapt to the ...
Hammer. Wang's user avatar
6 votes
2 answers
3k views

What is the difference between learning without forgetting and transfer learning?

I would like to incrementally train my model with my current dataset and I asked this question on Github, which is what I'm using SSD MobileNet v1. Someone there told me about learning without ...
abhimanyuaryan's user avatar
4 votes
1 answer
1k views

How can I incrementally train a Yolo model without catastrophic forgetting?

I have successfully trained a Yolo model to recognize k classes. Now I want to train by adding k+1 class to the pre-trained weights (k classes) without forgetting previous k classes. Ideally, I want ...
Troy's user avatar
  • 83
66 votes
4 answers
17k views

Are neural networks prone to catastrophic forgetting?

Imagine you show a neural network a picture of a lion 100 times and label it with "dangerous", so it learns that lions are dangerous. Now imagine that previously you have shown it millions ...
zooby's user avatar
  • 2,216
6 votes
1 answer
4k views

Can I train a neural network incrementally given new daily data?

I would like to know if it was possible to train a neural network on daily new data. Let me explain this more in detail. Let's say you have daily data from 2010 to 2019. You train your NN on all of it,...
neomatriciel's user avatar
2 votes
1 answer
388 views

How does FastText support online learning?

I'm using FastText pre-trained-embedding for tackling a classification task, but I saw it supports also online training (incremental training) for adding domain-specific corpus. How does it work? ...
Alfonso's user avatar
  • 65
4 votes
1 answer
1k views

Can a CNN be trained incrementally?

Like our human brain, we can first learn (train) the handwriting 0 and 1. After the traing (and test) accuray is good enough, we only need to study (traing) the hardwriting 2, Instead of cleaning all ...
Sunson29's user avatar
4 votes
0 answers
146 views

What are stable ways of doing online machine learning?

I am trying to deploy a machine learning solution online into an application for a client. One thing they requested is that the solution must be able to learn online because the problem may be non-...
Rui Nian's user avatar
  • 433
3 votes
3 answers
5k views

What is the name of an AI system that learns by trial and error?

Imagine a system that controls dampers in a complex vent system that has an objective to perfectly equalize the output from each vent. The system has sensors for damper position, flow at various ...
SchroedingersCat's user avatar
9 votes
2 answers
1k views

Are there dynamic neural networks?

Are there neural networks that can decide to add/delete neurons (or change the neuron models/activation functions or change the assigned meaning for neurons), links or even complete layers during ...
TomR's user avatar
  • 853
2 votes
1 answer
73 views

Will training an AI still work if the input data is somewhat sparse?

I'm looking at writing an AI agent for pattern recognition. I want to be able to constantly feed new data to the AI to continuously train it as new data may have new patterns. My problem, though, is ...
Alexis Wilke's user avatar
31 votes
5 answers
14k views

Is it possible to train a neural network as new classes are given?

I would like to train a neural network (NN) where the output classes are not (all) defined from the start. More and more classes will be introduced later based on incoming data. This means that, every ...
Fr_nkenstien's user avatar
10 votes
3 answers
1k views

What do you call a machine learning system that keeps on learning?

As I understand it from this video lecture, there are three types of deep learning: Supervised Unsupervised Reinforcement All these can serve to train a neural network either only prior to its ...
ZakC's user avatar
  • 347