The Subsequent AI Revolution: A Tutorial Utilizing VAEs to Generate Excessive-High quality Artificial Information

What’s artificial knowledge?

Information created by a pc supposed to duplicate or increase current knowledge.

Why is it helpful?

We’ve all skilled the success of ChatGPT, Llama, and extra lately, DeepSeek. These language fashions are getting used ubiquitously throughout society and have triggered many claims that we’re quickly approaching Synthetic Basic Intelligence — AI able to replicating any human operate.

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Earlier than getting too excited, or scared, relying in your perspective — we’re additionally quickly approaching a hurdle to the development of those language fashions. In keeping with a paper revealed by a gaggle from the analysis institute, Epoch [1], we’re operating out of information. They estimate that by 2028 we can have reached the higher restrict of doable knowledge upon which to coach language fashions.

Picture by Writer. Graph primarily based on estimated dataset projections. It is a reconstructed visualisation impressed by Epoch analysis group [1].

What occurs if we run out of information?

Effectively, if we run out of information then we aren’t going to have something new with which to coach our language fashions. These fashions will then cease bettering. If we need to pursue Synthetic Basic Intelligence then we’re going to should provide you with new methods of bettering AI with out simply rising the quantity of real-world coaching knowledge.

One potential saviour is artificial knowledge which may be generated to imitate current knowledge and has already been used to enhance the efficiency of fashions like Gemini and DBRX.

Artificial knowledge past LLMs

Past overcoming knowledge shortage for big language fashions, artificial knowledge can be utilized within the following conditions:

Delicate Information — if we don’t need to share or use delicate attributes, artificial knowledge may be generated which mimics the properties of those options whereas sustaining anonymity.
Costly knowledge — if gathering knowledge is pricey we will generate a big quantity of artificial knowledge from a small quantity of real-world knowledge.
Lack of information — datasets are biased when there’s a disproportionately low variety of particular person knowledge factors from a selected group. Artificial knowledge can be utilized to steadiness a dataset.

Imbalanced datasets

Imbalanced datasets can (*however not all the time*) be problematic as they might not comprise sufficient data to successfully practice a predictive mannequin. For instance, if a dataset comprises many extra males than girls, our mannequin could also be biased in the direction of recognising males and misclassify future feminine samples as males.

On this article we present the imbalance within the widespread UCI Grownup dataset [2], and the way we will use a variational auto-encoder to generate Artificial Information to enhance classification on this instance.

We first obtain the Grownup dataset. This dataset comprises options corresponding to age, schooling and occupation which can be utilized to foretell the goal end result ‘revenue’.

# Obtain dataset right into a dataframe
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/grownup/grownup.knowledge"
columns = [
   "age", "workclass", "fnlwgt", "education", "education-num", "marital-status",
   "occupation", "relationship", "race", "sex", "capital-gain",
   "capital-loss", "hours-per-week", "native-country", "income"
]
knowledge = pd.read_csv(url, header=None, names=columns, na_values=" ?", skipinitialspace=True)

# Drop rows with lacking values
knowledge = knowledge.dropna()

# Break up into options and goal
X = knowledge.drop(columns=["income"])
y = knowledge['income'].map({'>50K': 1, '<=50K': 0}).values

# Plot distribution of revenue
plt.determine(figsize=(8, 6))
plt.hist(knowledge['income'], bins=2, edgecolor="black")
plt.title('Distribution of Revenue')
plt.xlabel('Revenue')
plt.ylabel('Frequency')
plt.present()

Within the Grownup dataset, revenue is a binary variable, representing people who earn above, and under, $50,000. We plot the distribution of revenue over your entire dataset under. We are able to see that the dataset is closely imbalanced with a far bigger variety of people who earn lower than $50,000.

Picture by Writer. Unique dataset: Variety of knowledge situations with the label ≤50k and >50k. There’s a disproportionately bigger illustration of people who earn lower than 50k within the dataset.

Regardless of this imbalance we will nonetheless practice a machine studying classifier on the Grownup dataset which we will use to find out whether or not unseen, or check, people must be categorised as incomes above, or under, 50k.

# Preprocessing: One-hot encode categorical options, scale numerical options
numerical_features = ["age", "fnlwgt", "education-num", "capital-gain", "capital-loss", "hours-per-week"]
categorical_features = [
   "workclass", "education", "marital-status", "occupation", "relationship",
   "race", "sex", "native-country"
]

preprocessor = ColumnTransformer(
   transformers=[
       ("num", StandardScaler(), numerical_features),
       ("cat", OneHotEncoder(), categorical_features)
   ]
)

X_processed = preprocessor.fit_transform(X)

# Convert to numpy array for PyTorch compatibility
X_processed = X_processed.toarray().astype(np.float32)
y_processed = y.astype(np.float32)
# Break up dataset in practice and check units
X_model_train, X_model_test, y_model_train, y_model_test = train_test_split(X_processed, y_processed, test_size=0.2, random_state=42)


rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
rf_classifier.match(X_model_train, y_model_train)

# Make predictions
y_pred = rf_classifier.predict(X_model_test)

# Show confusion matrix
plt.determine(figsize=(6, 4))
sns.heatmap(cm, annot=True, fmt="d", cmap="YlGnBu", xticklabels=["Negative", "Positive"], yticklabels=["Negative", "Positive"])
plt.xlabel("Predicted")
plt.ylabel("Precise")
plt.title("Confusion Matrix")
plt.present()

Printing out the confusion matrix of our classifier reveals that our mannequin performs pretty nicely regardless of the imbalance. Our mannequin has an general error charge of 16% however the error charge for the optimistic class (revenue > 50k) is 36% the place the error charge for the unfavorable class (revenue < 50k) is 8%.

This discrepancy reveals that the mannequin is certainly biased in the direction of the unfavorable class. The mannequin is ceaselessly incorrectly classifying people who earn greater than 50k as incomes lower than 50k.

Beneath we present how we will use a Variational Autoencoder to generate artificial knowledge of the optimistic class to steadiness this dataset. We then practice the identical mannequin utilizing the synthetically balanced dataset and scale back mannequin errors on the check set.

Picture by Writer. Confusion matrix for predictive mannequin on authentic dataset.

How can we generate artificial knowledge?

There are many totally different strategies for producing artificial knowledge. These can embody extra conventional strategies corresponding to SMOTE and Gaussian Noise which generate new knowledge by modifying current knowledge. Alternatively Generative fashions corresponding to Variational Autoencoders or Basic Adversarial networks are predisposed to generate new knowledge as their architectures be taught the distribution of actual knowledge and use these to generate artificial samples.

On this tutorial we use a variational autoencoder to generate artificial knowledge.

Variational Autoencoders

Variational Autoencoders (VAEs) are nice for artificial knowledge technology as a result of they use actual knowledge to be taught a steady latent house. We are able to view this latent house as a magic bucket from which we will pattern artificial knowledge which carefully resembles current knowledge. The continuity of this house is one among their massive promoting factors because it means the mannequin generalises nicely and doesn’t simply memorise the latent house of particular inputs.

A VAE consists of an encoder, which maps enter knowledge right into a chance distribution (imply and variance) and a decoder, which reconstructs the info from the latent house.

For that steady latent house, VAEs use a reparameterization trick, the place a random noise vector is scaled and shifted utilizing the discovered imply and variance, guaranteeing clean and steady representations within the latent house.

Beneath we assemble a BasicVAE class which implements this course of with a easy structure.

The encoder compresses the enter right into a smaller, hidden illustration, producing each a imply and log variance that outline a Gaussian distribution aka creating our magic sampling bucket. As a substitute of straight sampling, the mannequin applies the reparameterization trick to generate latent variables, that are then handed to the decoder.
The decoder reconstructs the unique knowledge from these latent variables, guaranteeing the generated knowledge maintains traits of the unique dataset.

class BasicVAE(nn.Module):
   def __init__(self, input_dim, latent_dim):
       tremendous(BasicVAE, self).__init__()
       # Encoder: Single small layer
       self.encoder = nn.Sequential(
           nn.Linear(input_dim, 8),
           nn.ReLU()
       )
       self.fc_mu = nn.Linear(8, latent_dim)
       self.fc_logvar = nn.Linear(8, latent_dim)
      
       # Decoder: Single small layer
       self.decoder = nn.Sequential(
           nn.Linear(latent_dim, 8),
           nn.ReLU(),
           nn.Linear(8, input_dim),
           nn.Sigmoid()  # Outputs values in vary [0, 1]
       )

   def encode(self, x):
       h = self.encoder(x)
       mu = self.fc_mu(h)
       logvar = self.fc_logvar(h)
       return mu, logvar

   def reparameterize(self, mu, logvar):
       std = torch.exp(0.5 * logvar)
       eps = torch.randn_like(std)
       return mu + eps * std

   def decode(self, z):
       return self.decoder(z)

   def ahead(self, x):
       mu, logvar = self.encode(x)
       z = self.reparameterize(mu, logvar)
       return self.decode(z), mu, logvar

Given our BasicVAE structure we assemble our loss capabilities and mannequin coaching under.

def vae_loss(recon_x, x, mu, logvar, tau=0.5, c=1.0):
   recon_loss = nn.MSELoss()(recon_x, x)
 
   # KL Divergence Loss
   kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
   return recon_loss + kld_loss / x.measurement(0)

def train_vae(mannequin, data_loader, epochs, learning_rate):
   optimizer = optim.Adam(mannequin.parameters(), lr=learning_rate)
   mannequin.practice()
   losses = []
   reconstruction_mse = []

   for epoch in vary(epochs):
       total_loss = 0
       total_mse = 0
       for batch in data_loader:
           batch_data = batch[0]
           optimizer.zero_grad()
           reconstructed, mu, logvar = mannequin(batch_data)
           loss = vae_loss(reconstructed, batch_data, mu, logvar)
           loss.backward()
           optimizer.step()
           total_loss += loss.merchandise()

           # Compute batch-wise MSE for comparability
           mse = nn.MSELoss()(reconstructed, batch_data).merchandise()
           total_mse += mse

       losses.append(total_loss / len(data_loader))
       reconstruction_mse.append(total_mse / len(data_loader))
       print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss:.4f}, MSE: {total_mse:.4f}")
   return losses, reconstruction_mse

combined_data = np.concatenate([X_model_train.copy(), y_model_train.cop
y().reshape(26048,1)], axis=1)

# Prepare-test cut up
X_train, X_test = train_test_split(combined_data, test_size=0.2, random_state=42)

batch_size = 128

# Create DataLoaders
train_loader = DataLoader(TensorDataset(torch.tensor(X_train)), batch_size=batch_size, shuffle=True)
test_loader = DataLoader(TensorDataset(torch.tensor(X_test)), batch_size=batch_size, shuffle=False)

basic_vae = BasicVAE(input_dim=X_train.form[1], latent_dim=8)

basic_losses, basic_mse = train_vae(
   basic_vae, train_loader, epochs=50, learning_rate=0.001,
)

# Visualize outcomes
plt.determine(figsize=(12, 6))
plt.plot(basic_mse, label="Fundamental VAE")
plt.ylabel("Reconstruction MSE")
plt.title("Coaching Reconstruction MSE")
plt.legend()
plt.present()

vae_loss consists of two elements: reconstruction loss, which measures how nicely the generated knowledge matches the unique enter utilizing Imply Squared Error (MSE), and KL divergence loss, which ensures that the discovered latent house follows a traditional distribution.

train_vae optimises the VAE utilizing the Adam optimizer over a number of epochs. Throughout coaching, the mannequin takes mini-batches of information, reconstructs them, and computes the loss utilizing vae_loss. These errors are then corrected by way of backpropagation the place the mannequin weights are up to date. We practice the mannequin for 50 epochs and plot how the reconstruction imply squared error decreases over coaching.

We are able to see that our mannequin learns rapidly easy methods to reconstruct our knowledge, evidencing environment friendly studying.

Picture by Writer. Reconstruction MSE of BasicVAE on the Grownup dataset.

Now we’ve skilled our BasicVAE to precisely reconstruct the Grownup dataset we will now use it to generate artificial knowledge. We need to generate extra samples of the optimistic class (people who earn over 50k) with the intention to steadiness out the courses and take away the bias from our mannequin.

To do that we choose all of the samples from our VAE dataset the place revenue is the optimistic class (earn greater than 50k). We then encode these samples into the latent house. As we’ve solely chosen samples of the optimistic class to encode, this latent house will mirror properties of the optimistic class which we will pattern from to create artificial knowledge.

We pattern 15000 new samples from this latent house and decode these latent vectors again into the enter knowledge house as our artificial knowledge factors.

# Create column names
col_number = sample_df.form[1]
col_names = [str(i) for i in range(col_number)]
sample_df.columns = col_names

# Outline the function worth to filter
feature_value = 1.0  # Specify the function worth - right here we set the revenue to 1

# Set all revenue values to 1 : Over 50k
selected_samples = sample_df[sample_df[col_names[-1]] == feature_value]
selected_samples = selected_samples.values
selected_samples_tensor = torch.tensor(selected_samples, dtype=torch.float32)

basic_vae.eval()  # Set mannequin to analysis mode
with torch.no_grad():
   mu, logvar = basic_vae.encode(selected_samples_tensor)
   latent_vectors = basic_vae.reparameterize(mu, logvar)

# Compute the imply latent vector for this function
mean_latent_vector = latent_vectors.imply(dim=0)


num_samples = 15000  # Variety of new samples
latent_dim = 8
latent_samples = mean_latent_vector + 0.1 * torch.randn(num_samples, latent_dim)

with torch.no_grad():
   generated_samples = basic_vae.decode(latent_samples)

Now we’ve generated artificial knowledge of the optimistic class, we will mix this with the unique coaching knowledge to generate a balanced artificial dataset.

new_data = pd.DataFrame(generated_samples)

# Create column names
col_number = new_data.form[1]
col_names = [str(i) for i in range(col_number)]
new_data.columns = col_names

X_synthetic = new_data.drop(col_names[-1],axis=1)
y_synthetic = np.asarray([1 for _ in range(0,X_synthetic.shape[0])])

X_synthetic_train = np.concatenate([X_model_train, X_synthetic.values], axis=0)
y_synthetic_train = np.concatenate([y_model_train, y_synthetic], axis=0)

mapping = {1: '>50K', 0: '<=50K'}
map_function = np.vectorize(lambda x: mapping[x])
# Apply mapping
y_mapped = map_function(y_synthetic_train)

plt.determine(figsize=(8, 6))
plt.hist(y_mapped, bins=2, edgecolor="black")
plt.title('Distribution of Revenue')
plt.xlabel('Revenue')
plt.ylabel('Frequency')
plt.present()

Picture by Writer. Artificial dataset: Variety of knowledge situations with the label ≤50k and >50k. There at the moment are a balanced variety of people incomes extra and fewer than 50k.

We are able to now use our balanced coaching artificial dataset to retrain our random forest classifier. We are able to then consider this new mannequin on the unique check knowledge to see how efficient our artificial knowledge is at decreasing the mannequin bias.

rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
rf_classifier.match(X_synthetic_train, y_synthetic_train)

# Step 5: Make predictions
y_pred = rf_classifier.predict(X_model_test)

cm = confusion_matrix(y_model_test, y_pred)

# Create heatmap
plt.determine(figsize=(6, 4))
sns.heatmap(cm, annot=True, fmt="d", cmap="YlGnBu", xticklabels=["Negative", "Positive"], yticklabels=["Negative", "Positive"])
plt.xlabel("Predicted")
plt.ylabel("Precise")
plt.title("Confusion Matrix")
plt.present()

Our new classifier, skilled on the balanced artificial dataset makes fewer errors on the unique check set than our authentic classifier skilled on the imbalanced dataset and our error charge is now decreased to 14%.

Picture by Writer. Confusion matrix for predictive mannequin on artificial dataset.

Nonetheless, we’ve not been capable of scale back the discrepancy in errors by a big quantity, our error charge for the optimistic class continues to be 36%. This could possibly be resulting from to the next causes:

We’ve mentioned how one of many advantages of VAEs is the training of a steady latent house. Nonetheless, if the bulk class dominates, the latent house may skew in the direction of the bulk class.
The mannequin might not have correctly discovered a definite illustration for the minority class as a result of lack of information, making it exhausting to pattern from that area precisely.

On this tutorial we’ve launched and constructed a BasicVAE structure which can be utilized to generate artificial knowledge which improves the classification accuracy on an imbalanced dataset.

Observe for future articles the place I’ll present how we will construct extra refined VAE architectures which handle the above issues with imbalanced sampling and extra.

[1] Villalobos, P., Ho, A., Sevilla, J., Besiroglu, T., Heim, L., & Hobbhahn, M. (2024). Will we run out of information? Limits of LLM scaling primarily based on human-generated knowledge. arXiv preprint arXiv:2211.04325, 3.

[2] Becker, B. & Kohavi, R. (1996). Grownup [Dataset]. UCI Machine Studying Repository. https://doi.org/10.24432/C5XW20.