Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inverse Problems Leveraging Pre-trained Contrastive Representations   We study a new family of inverse problems for recovering representations of
corrupted data. We assume access to a pre-trained representation learning
network R(x) that operates on clean images, like CLIP. The problem is to
recover the representation of an image R(x), if we are only given a corrupted
version A(x), for some known forward operator A. We propose a supervised
inversion method that uses a contrastive objective to obtain excellent
representations for highly corrupted images. Using a linear probe on our robust
representations, we achieve a higher accuracy than end-to-end supervised
baselines when classifying images with various types of distortions, including
blurring, additive noise, and random pixel masking. We evaluate on a subset of
ImageNet and observe that our method is robust to varying levels of distortion.
Our method outperforms end-to-end baselines even with a fraction of the labeled
data in a wide range of forward operators.

24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an