DCGANs with architectural constraints learn a hierarchy of representations from object parts to scenes in both generator and discriminator across image datasets.
Empirical evalua- tion of rectified activations in convolutional network
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
representative citing papers
ABGD parametrizes piecewise linear functions as difference of max-affine functions and converges linearly to an epsilon-accurate solution with O(d max(sigma/epsilon,1)^2) samples under sub-Gaussian noise, which is minimax optimal up to logs.
Automated search discovers Swish activation f(x) = x * sigmoid(βx) that improves top-1 ImageNet accuracy over ReLU by 0.9% on Mobile NASNet-A and 0.6% on Inception-ResNet-v2.
A two-module neural model disentangles spatial layout from material properties to generate controllable and more realistic room impulse responses, reporting gains of up to 16% on acoustic metrics and 70% on material metrics plus better human ratings.
A functional similarity metric for ReLU networks uses normalized activation region signatures and MinHash to overcome parametric symmetries like neuron permutation and scaling.
LAGCD inserts residual linear adapters into each ViT block plus a distribution alignment loss to improve generalized category discovery by increasing model flexibility while reducing bias between seen and novel classes.
citing papers explorer
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
DCGANs with architectural constraints learn a hierarchy of representations from object parts to scenes in both generator and discriminator across image datasets.
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Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions
ABGD parametrizes piecewise linear functions as difference of max-affine functions and converges linearly to an epsilon-accurate solution with O(d max(sigma/epsilon,1)^2) samples under sub-Gaussian noise, which is minimax optimal up to logs.
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Searching for Activation Functions
Automated search discovers Swish activation f(x) = x * sigmoid(βx) that improves top-1 ImageNet accuracy over ReLU by 0.9% on Mobile NASNet-A and 0.6% on Inception-ResNet-v2.
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Materialistic RIR: Material Conditioned Realistic RIR Generation
A two-module neural model disentangles spatial layout from material properties to generate controllable and more realistic room impulse responses, reporting gains of up to 16% on acoustic metrics and 70% on material metrics plus better human ratings.
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Functional Similarity Metric for Neural Networks: Overcoming Parametric Ambiguity via Activation Region Analysis
A functional similarity metric for ReLU networks uses normalized activation region signatures and MinHash to overcome parametric symmetries like neuron permutation and scaling.
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Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery
LAGCD inserts residual linear adapters into each ViT block plus a distribution alignment loss to improve generalized category discovery by increasing model flexibility while reducing bias between seen and novel classes.