Published inArtificialisCh 10: Vision Transformer Part II — Iterative Erasing of Unattended Image Regions in PyTorchHelping the model better detect objects in images by iteratively erasing (i.e. darkening) regions of the image unattended by ViT using its…Jan 28, 20221Jan 28, 20221
Ch 9. Vision Transformer Part I— Introduction and Fine-Tuning in PyTorchHow using self-attention for image classification reduces inductive bias inherent to CNNs including translation equivariance and locality…Jan 28, 20222Jan 28, 20222
Ch 8. Adversarial Discriminative Domain Adaptation (ADDA): Quest for Semantic AlignmentOptimizing domain adaptation through toggling data annotation, training frameworks, and pre-training datasetsDec 23, 2021Dec 23, 2021
Published inCodeXCh 7. Decoding Black Box of CNNs using Feature Map VisualizationsHow to ask CNN architectures useful questions to get insights about their behavioursOct 19, 2021Oct 19, 2021
Ch 6. Optimizing Data for Flexible Image RecognitionHow can we adjust input data and labels to encourage neural networks to “perceive” images flexibly as humans do?Oct 10, 20211Oct 10, 20211
Ch 5. t-SNE Plots as a Human-AI Translatort-SNE Plots as a means of communicating with a deep learning modelSep 22, 2021Sep 22, 2021
Ch 4. Transfer Learning with ResNet50 Part II- Model Analysis to Unexpected RiddleThinking about the Procedure >> Following the ProcedureSep 21, 20211Sep 21, 20211
Ch 3. Transfer Learning with ResNet50 Part I — from Dataloaders to TrainingSeed of Thought : Just how much about the ML model do we know after looking at the confusion matrix?Sep 18, 20212Sep 18, 20212
Ch 2. Iterative Data CollectionHow to creatively design data for your ML problemSep 15, 20211Sep 15, 20211
