Well, it’s basically taking an existing model (like a pretrained language or vision model) and fine-tuning it to perform multiple tasks at once like navigating through a maze while also identifying objects along the way!
But why would we want to do this? Well, for starters, it can help improve the accuracy of our models by allowing them to learn from more diverse data. Plus, it’s just plain cool to see how these AI systems can handle multiple tasks at once!
So, Time to get going with some examples and code snippets to really get a feel for what multi-task finetuning is all about. First up, we have the classic “Navigate through a maze while identifying objects” task. Here’s an example script using PyTorch:
# Load pretrained model (in this case, ResNet50) and convert to multi-task finetuning mode
model = torch.hub.load('pytorch/vision:v0.12', 'resnet50') # Load the ResNet50 model from the PyTorch vision library
num_classes = 10 # Number of object classes in our dataset
for param in model.parameters():
param.requires_grad = False # Freeze the parameters of the pretrained model to prevent them from being updated during training
new_params = []
for name, param in model.named_children()[0].named_parameters(): # Iterate through the first child module of the model (the feature extractor)
if 'fc' in name: # Check if the current parameter is the fully connected layer
new_params += [param] # Add the fully connected layer to the list of new parameters
model.classifier = nn.Sequential(*list(model.classifier.children())[:-1], # Remove the last linear layer (which we will replace with our own)
nn.Linear(in_features=new_params[-1].weight.shape[1], out_features=num_classes)) # Add a new linear layer with the same input size as the last layer of the feature extractor and the specified number of output classes
# Load dataset and split into training/validation sets
train_loader, val_loader = load_data() # Load the dataset and split it into training and validation sets
# Train model on both tasks (navigation and object identification) simultaneously using a multi-task loss function
criterion = nn.MSELoss() + torch.nn.CrossEntropyLoss(num_classes=num_classes) # Combine navigation and object identification losses into one multi-task loss function
optimizer = optim.Adam(model.parameters(), lr=0.01, weight_decay=0.0005) # Use the Adam optimizer with a learning rate of 0.01 and weight decay of 0.0005
for epoch in range(20):
for batch_idx, (data, target) in enumerate(train_loader):
# Forward pass: Compute predicted navigation and object identification outputs using the model
output = model(data) # Pass the input data through the model to get the predicted outputs
# Calculate loss based on both tasks (navigation and object identification)
loss = criterion(output['nav'], target['nav']) + criterion(output['obj_id'], target['obj_id']) # Calculate the loss for both tasks and add them together
# Backward pass: Update the weights of the model to minimize the loss
optimizer.zero_grad() # Clear the gradients from the previous iteration
loss.backward() # Backpropagate the loss through the model
optimizer.step() # Update the weights of the model using the calculated gradients
# Evaluate performance on validation set after each epoch
val_loss = 0
for batch_idx, (data, target) in enumerate(val_loader):
output = model(data) # Pass the input data through the model to get the predicted outputs
val_loss += criterion(output['nav'], target['nav']) + criterion(output['obj_id'], target['obj_id']) # Calculate the loss for both tasks and add them together
print('Epoch {}/{}: Navigation loss {:.4f}, Object identification loss {:.4f}'.format(epoch+1, num_epochs, val_loss[0]/len(val_loader), val_loss[1]/len(val_loader))) # Print the average loss for each task on the validation set after each epoch
And there you have it multi-task finetuning for visual navigation! Of course, this is just one example and the specific implementation will vary depending on your dataset and task. But hopefully this gives you a good idea of what’s possible with this technique!