Under the summer project at my institute IIT Kanpur "Model Zoo", we are learning and practising to implement and summarise various deep learning architectures and models.
Here is a list of the implemenations:
- Resnet : Original Paper (Implemented)
- Based on CNN, aimed to tackle the difficulty of training deeper networks without rise in training and testing error.
- InfoGAN : Original Paper (Implemented)
- Based on DCGan, aimed at disentangled representation so that network learns systematically various meaningful features of the input sample.
- ALBERT : Original Paper (Under implementation)
- Based on BERT, it addresses the GPU/TPU memory limitations and training complexities of the the former by proposing two parameter-reduction techniques, which lowers memory, increases training speed and even achieves greater accuracy.
- Image Captioning : Original Paper (Implemented)
- Based on a novel combination of CNN over images and bidirectional RNN (LSTM) over captions, using output of encoder CNN as embeddings for the decoder RNN to generate rich descriptions of image regions.
- GloVe : Original Paper (Implemented)
- GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.