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📖 M. Tech in Artificial Intelligence @ Indian Institute of Science, Bangalore.
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📖 B. Tech in Electrical and Electronics Engineering @ Indian Institute of Technology, Guwahati.
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🎓 Research on Machine Unlearning in Generative Models @ the IACV Lab.
| Title | Description | Tags |
|---|---|---|
| Devanagari Character Generator | Generate alphabets and numbers from devnagri script using GAN and its different variations | GAN, DCGAN, Generative Models |
| Segmentation of Cricket Images | Image segmentation of cricket images, players, equipment, and field elements using a pretrained model. | ResNet50, Semantic Segmentation |
| Object Detection using YOLOv7 | I performed the task of object detection on a custom dataset using YOLO v7 and compared the model performance with change in hyper-parameters. | YOLO, Object Detection |
| Fast Convolution Methods | Implementation of Depthwise Convolution to speed up CNNs | CNN, Convolution |
| Custom JPEG Implementation | I implemented the JPEG image compression algorithm from scratch and compared the compression results with standard library function of OpenCV | JPEG, Image Compression |
| Title | Description | Tags |
|---|---|---|
| Transliteration of Names | I performed the task of sequence-to-sequence mapping using 3 generation of Encoder-Decoder architectures namely RNN, LSTM and Transformers | Seq2seq LSTM RNN |
| Custom GPT-2 Tokenizer | I built a gpt-2 style tokenizer that can be trained on any .txt data to generate tokens | Tokenization BPE |
| Title | Description | Tags |
|---|---|---|
| Classical ML Algorithms | Implementation of standard ml algorithms for regression, classification and clustering | Regression, Clustering, Classification |
| NN using Numpy | I made a Multi Layer Perceptron(fwd & bkwd pass) entirely in numpy using standard loss and activations | MLP, Backpropogation |
| community-detection | Implemented couple of classical graph algorithms for purpose of community detections | Graphs, Louvain, Girvan Newman |
| Title | Repo | Description | Tags |
|---|---|---|---|
| Efficient Estimation of Word Representations in Vector Space (2013) | Link | Introduced word2vec (CBOW and Skip-gram) to learn word embeddings that capture semantic relationships of words | NLP Word Embeddings Word2vec |
| Neural Machine Translation of Rare Words with Subword Units (2015) | Link | Introduced BPE Algorithm for sub-word tokenization | NLP Tokenization |
| Neural Machine Translation by Jointly Learning to Align and Translate (2016) | Link | Introduced attention for first time in RNN seq2seq architecture | NLP Attention RNN seq2seq |
| Attention is all you need (2017) | Link | First Paper that proposed Transformer architecture to perform sequence to sequence mapping | NLP Transformers seq-2-seq |
| Title | Repo | Description | Tags |
|---|---|---|---|
| Distinctive Image Features from Scale-Invariant Keypoints | Link | The original SIFT paper. Describes method for extraction of image features(deterministically), which are invariant to changes in scale, rotation, and illumination | Img Processing Feature Extraction |
| Noise removal via Bayesian wavelet coring & Adapting to unknown smoothness via wavelet shrinkage |
Link | Some Classical Techniques to perform Image denoising using Laplacian and Shrinkage extimates | Img Processing Denoising |
| Normalized Cuts and Image Segmentation | Link | Proposes an approach to image segmentation by framing it as a graph partitioning problem and introduces an algorithm to achieve that | Img Processing Segmentation |
| Fully Convolutional Networks for Semantic Segmentation | Link | Proposes FCN architecture for the purpose of Image segmentation | CV CNN Segmentation |
| Gradient-based learning applied to document recognition | Link | Proposes Le-Net architecture for the purpose of Document character recogonition | CV CNN Object detection |