**LeNet** is a classic convolutional neural network employing the use of convolutions, pooling and fully connected layers. It was used for the handwritten digit recognition task with the MNIST dataset. The architectural design served as inspiration for future networks such as [AlexNet](https://paperswithcode.com/method/alexnet) and [VGG](https://paperswithcode.com/method/vgg)..

[code](https://github.com/Elman295/Paper_with_code/blob/main/LeNet_5_Pytorch.ipynb)

**Augmented SBERT** is a data augmentation strategy for pairwise sentence scoring that uses a [BERT](https://paperswithcode.com/method/bert) cross-encoder to improve the performance for the [SBERT](https://paperswithcode.com/method/sbert) bi-encoders. Given a pre-trained, well-performing crossencoder, we sample sentence pairs according to a certain sampling strategy and label these using the cross-encoder. We call these weakly labeled examples the silver dataset and they will be merged with the gold training dataset. We then train the bi-encoder on this extended training dataset.

Augmented SBERT

Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks

LeNet

**FastSpeech 2s** is a text-to-speech model that abandons mel-spectrograms as intermediate output completely and directly generates speech waveform from text during inference. In other words there is no cascaded mel-spectrogram generation (acoustic model) and waveform generation (vocoder). FastSpeech 2s generates waveform conditioning on intermediate hidden, which makes it more compact in inference by discarding the mel-spectrogram decoder.

Two main design changes are made to the waveform decoder. 

First, considering that the phase information is difficult to predict using a variance predictor, [adversarial training](https://paperswithcode.com/methods/category/adversarial-training)  is used in the waveform decoder to force it to implicitly recover the phase information by itself. 

Secondly, the mel-spectrogram decoder of [FastSpeech 2](https://paperswithcode.com/method/fastspeech-2) is leveraged, which is trained on the full text sequence to help on the text feature extraction. As shown in the Figure, the waveform decoder is based on the structure of [WaveNet](https://paperswithcode.com/method/wavenet) including non-causal convolutions and gated activation. The waveform decoder takes a sliced hidden sequence corresponding to a short audio clip as input and upsamples it with transposed 1D-convolution to match the length of audio clip. The discriminator in the adversarial training adopts the same structure in Parallel WaveGAN, which consists of ten layers of non-causal [dilated 1-D convolutions](https://paperswithcode.com/method/dilated-convolution) with [leaky ReLU](https://paperswithcode.com/method/leaky-relu) activation function. The waveform decoder is optimized by the multi-resolution STFT loss and the [LSGAN discriminator](https://paperswithcode.com/method/lsgan) loss following Parallel WaveGAN. 

In inference, the mel-spectrogram decoder is discarded and only the waveform decoder is used to synthesize speech audio.

Year	1998
Data Source	CC BY-SA - https://paperswithcode.com