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Found 10 papers, 3 papers with code
Domain Aligned CLIP for Few-shot Classification
Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from inter-modal (image-text) alignment via contrastive objectives.
Dynamic Inference with Neural Interpreters
Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution.
Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution.
Function Contrastive Learning of Transferable Meta-Representations
This \emph{meta-representation}, which is computed from a few observed examples of the underlying function, is learned jointly with the predictive model.
Function Contrastive Learning of Transferable Representations
Few-shot-learning seeks to find models that are capable of fast-adaptation to novel tasks which are not encountered during training.
S2RMs: Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution.
Disentangled State Space Representations
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist.
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.
Kernel Mean Matching for Content Addressability of GANs
We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e. g., a generative adversarial network (GAN).
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution
Moreover, we show that a texture representation of those deep features better capture the perceptual quality of an image than the original deep features.
