Ground Truth is REALLY important for rational type
2024-03-04
In INTP as a rational type, we have Ti dominant, Ne auxiliary, Si tertiary, and Fe inferior. At first, I wondered why there is a Si tertiary because we don’t usually like repetitive things. But, then after I learned about GAN (General Adversarial Network) it’s really important to feed that discriminative model with new “ground truth” so the judgment from it is always accurate.
Same as our first function. We have Ti as our first function, used to recognize consistent and rational patterns from things. If we just feed that Ti function with “self-generated” input from our Ne function (imagination) then we slowly lose the ability to recognize a “real consistent pattern”.
So that when the Si function comes in. We would constantly retrieve our sensory experience from our past memory and compare it with our imagination (Ne function) so then our Ti would always have it accuracy.
The conversation below is a little discussion regarding GAN model with “Mistral Large” LLM
Habib: Hi
Mistral: Hello! How can I help you today? Is there something specific you would like to talk about or ask about? I'm here to provide information and answer any questions you have to the best of my ability.
Habib: Can you explain to me about GAN model?
Mistral: Sure! GAN stands for Generative Adversarial Network. It is a type of machine learning model that is used for generating new data that is similar to some existing dataset. GANs consist of two parts: a generator and a discriminator.
The generator is a neural network that is trained to generate new data samples that are similar to the real data. It takes a random noise vector as input and produces a fake data sample as output.
The discriminator is another neural network that is trained to distinguish between real and fake data samples. It takes a data sample as input (either real or fake) and outputs a probability that the sample is real.
During training, the generator and discriminator are trained together in an adversarial process. The generator tries to generate fake data that is as realistic as possible, while the discriminator tries to correctly classify real and fake data. As the training progresses, the generator gets better at generating realistic data, and the discriminator gets better at distinguishing between real and fake data.
Eventually, the generator becomes so good at generating realistic data that the discriminator is no longer able to accurately distinguish between real and fake data. At this point, the generator can be used to generate new data samples that are similar to the real data.
GANs have been used for a variety of applications, such as generating realistic images, creating synthetic data for training other machine learning models, and even generating music and videos.
Does that help? Do you have any other questions about GANs?
Habib: Does it using tokenization?
Mistral: No, GANs do not use tokenization as part of their architecture. Tokenization is a process of converting text data into numerical form by breaking it down into smaller pieces, such as words or characters, and assigning a unique numerical identifier to each piece. This is commonly used in natural language processing (NLP) tasks, such as text classification or language modeling.
GANs, on the other hand, are primarily used for generating new data samples that are similar to some existing dataset. They do not involve any text processing or tokenization. Instead, they operate directly on the raw data, such as images or audio signals.
That being said, it is possible to use GANs in combination with tokenization for certain tasks. For example, if you wanted to generate new text samples that are similar to some existing dataset, you could first tokenize the text data and then use a GAN to generate new sequences of tokens. But the GAN itself would not be responsible for the tokenization process.
Habib: What is the discriminator architecture?
Mistral: The architecture of the discriminator in a GAN can vary depending on the specific application and the type of data being used. However, in general, the discriminator is a neural network that takes a data sample as input (either real or fake) and outputs a probability that the sample is real.
Here is a possible architecture for a discriminator network:
- Input layer: This layer receives the input data sample, which could be an image, a sequence of audio samples, or some other type of data.
- Convolutional layer(s): If the input data is an image, the discriminator may use one or more convolutional layers to extract features from the image. These layers typically use a combination of convolutional filters, activation functions, and pooling operations to identify patterns in the input data.
- Fully connected layer(s): After the convolutional layers, the discriminator may use one or more fully connected layers to process the features extracted from the input data. These layers are typically used to combine the features and produce a single output value.
- Output layer: The output layer of the discriminator is a single sigmoid unit, which produces a probability value between 0 and 1 indicating whether the input data is real or fake.
During training, the discriminator is trained to minimize the binary cross-entropy loss between its predicted probabilities and the true labels (real or fake) of the input data. As the training progresses, the discriminator becomes better at distinguishing between real and fake data, which in turn helps the generator to produce more realistic data samples.
Does that help? Do you have any other questions about the discriminator architecture in GANs?
Habib: So, the discriminator always needs new "ground truth" to be able to keep reliable?
Mistral: Yes, that's correct! The discriminator in a GAN needs to be trained on a dataset of real samples in order to learn to distinguish between real and fake data. As the generator improves and starts producing more realistic samples, the discriminator needs to continue learning from new real samples in order to maintain its accuracy.
In other words, the discriminator needs to be trained on a dataset that is representative of the true data distribution in order to be able to accurately classify new samples as real or fake. If the discriminator is only trained on a limited or biased dataset, it may not be able to generalize well to new data and may produce inaccurate classifications.
During training, the generator and discriminator are typically trained in an alternating fashion. The generator produces a batch of fake samples, which are then used to train the discriminator along with a batch of real samples. The discriminator's feedback is then used to update the generator's weights, and the process is repeated for multiple iterations.
By continuously training on new real samples and adapting to the improving generator, the discriminator can maintain its accuracy and provide useful feedback to the generator.
Does that answer your question?