… We also compare against a post-processed version of our model, where we train a deep
fried convnet and then apply SVD plus fine-tuning to the final softmax layer, which further re…

… ConvNets we call Similarity Networks (SimNets), that preserves the simplicity and effectiveness
of ConvNets, … for designing deep networks with a higher abstraction level than ConvNets, …

… For the Fastfood approximation in Deep Fried Convnets we consider that φff is gained with
one stack of random features to form V in equation 3, except in the experiments of section 4.3 …

… To address the two challenges above, we propose a novel approach, Fried Binary … We
call our approach as Fried Binary Embedding (FBE) following Deep Fried Convnets [24] …

… We evaluate our compression scheme on eight deep learning image benchmark data sets
and compare against four competitive baselines. Although all compression schemes lead to …

… We call our approaches as Fried Binary Embedding following Deep Fried Convnets [17]
and Circulant Binary Embedding [12]. Extensive experiments show that our approach not only …

… We evaluate our compression scheme on eight deep learning image benchmark data sets
and compare against four competitive baselines. Although all compression schemes lead to …

… layers, ie these are classical convnets architectures ; (2) Deep Fried implements the Fastfood
… For the Fastfood approximation in Deep Fried Convnets we consider that φff is gained with …

We propose unitary group convolutions (UGConvs), a building block for CNNs which
compose a group convolution with unitary transforms in feature space to learn a richer set of …

In this paper, we apply a convolutional neural network (CNN) to the tasks of detecting and
recognizing food images. Because of the wide diversity of types of food, image recognition of …