Ayush Thakur
  • About Me
  • Authoring
    • DeepFaceDrawing: An Overview
    • Rewriting a Deep Generative Model: An Overview
    • Unsupervised Visual Representation Learning with SwAV
    • In-Domain GAN Inversion for Real Image Editing
    • Metric Learning for Image Search
    • Object Localization with Keras and W&B
    • Image Segmentation Using Keras and W&B
    • Understanding the Effectivity of Ensembles in DL
    • Modern Data Augmentation Techniques for CV
    • Adversarial Latent Autoencoders
    • Towards Deep Generative Modeling with W&B
    • Interpretability in Deep Learning - CAM and GradCAM
    • Introduction to image inpainting with deep learning
    • Simple Ways to Tackle Class Imbalance
    • Debugging Neural Networks with PyTorch
    • Generating Digital Painting Lighting Effects
    • Multi Task Learning with W&B
    • Translate American Sign Language Using CNN
    • Converting FC Layers to Conv Layers
  • Projects
    • Smart Traffic Management Using Reinforcement Learning
    • Sign Language Translator
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  • ​📈 Read the article here.
  • ​👀 Check out the GitHub repo here.

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Multi Task Learning with W&B

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Last updated 4 years ago

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In this post, I’ll walk you through my project "Faceless”. Some of the ideas are inspired from this article, . We’ll apply these best practices around formulating your problem and will extensively cover multi-output classification. Weights & Biases was super useful in iterating through model architectures quickly and finding a good architecture for this project, and also in monitoring model performance. You can find the code for the project , and the W&B dashboard with the metrics .

​📈 Read the article .

​👀 Check out the GitHub repo .

Formulate your problem as an ML problem
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