The **ENet Initial Block** is an image model block used in the [ENet](https://paperswithcode.com/method/enet) semantic segmentation architecture. [Max Pooling](https://paperswithcode.com/method/max-pooling) is performed with non-overlapping 2 × 2 windows, and the [convolution](https://paperswithcode.com/method/convolution) has 13 filters, which sums up to 16 feature maps after concatenation. This is heavily inspired by Inception Modules.

Z. Průša, P. Balazs and P. L. Søndergaard, "A Noniterative Method for Reconstruction of Phase From STFT Magnitude," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 5, pp. 1154-1164, May 2017, doi: 10.1109/TASLP.2017.2678166.
Abstract: A noniterative method for the reconstruction of the short-time fourier transform (STFT) phase from the magnitude is presented. The method is based on the direct relationship between the partial derivatives of the phase and the logarithm of the magnitude of the un-sampled STFT with respect to the Gaussian window. Although the theory holds in the continuous setting only, the experiments show that the algorithm performs well even in the discretized setting (discrete Gabor transform) with low redundancy using the sampled Gaussian window, the truncated Gaussian window and even other compactly supported windows such as the Hann window. Due to the noniterative nature, the algorithm is very fast and it is suitable for long audio signals. Moreover, solutions of iterative phase reconstruction algorithms can be improved considerably by initializing them with the phase estimate provided by the present algorithm. We present an extensive comparison with the state-of-the-art algorithms in a reproducible manner.
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7890450&isnumber=7895265

PGHI

ENet Initial Block

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

**MDETR** is an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. It utilizes a [transformer](https://paperswithcode.com/method/transformer)-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. The network is pre-trained on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. The network is then fine-tuned on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation.

Source	ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com