2.3k citations · 2.9k across the 31 of their papers we have counts for
9 papers · 1 filter
Fine-tuning Image Transformers using Learnable Memory
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov +1
In this paper we propose augmenting Vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optiona…
BasisNet: Two-stage Model Synthesis for Efficient Inference
Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov +6
In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form.…
Image segmentation via Cellular Automata
Mark Sandler, Andrey Zhmoginov, Liangcheng Luo +3
In this paper, we propose a new approach for building cellular automata to solve real-world segmentation problems. We design and train a cellular automaton that can successfully se…
Non-discriminative data or weak model? On the relative importance of data and model resolution
Mark Sandler, Jonathan Baccash, Andrey Zhmoginov +1
We explore the question of how the resolution of the input image ("input resolution") affects the performance of a neural network when compared to the resolution of the hidden laye…
Information-Bottleneck Approach to Salient Region Discovery
Andrey Zhmoginov, Ian Fischer, Mark Sandler
We propose a new method for learning image attention masks in a semi-supervised setting based on the Information Bottleneck principle. Provided with a set of labeled images, the ma…
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu +2
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across…
CycleGAN, a Master of Steganography
Casey Chu, Andrey Zhmoginov, Mark Sandler
CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing pro…
The Power of Sparsity in Convolutional Neural Networks
Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov
Deep convolutional networks are well-known for their high computational and memory demands. Given limited resources, how does one design a network that balances its size, training…
Inverting face embeddings with convolutional neural networks
Andrey Zhmoginov, Mark Sandler
Deep neural networks have dramatically advanced the state of the art for many areas of machine learning. Recently they have been shown to have a remarkable ability to generate high…